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Deep Learning

1276 articles · Page 3 of 13
Data Thinking Notes
Data Thinking Notes
Apr 6, 2025 · Artificial Intelligence

Why Mixture of Experts (MoE) is Revolutionizing Large AI Models

Mixture of Experts (MoE) leverages dynamic conditional computation and specialized expert networks to overcome the parameter explosion and inefficiency of dense models, offering scalable capacity, multi‑task adaptability, and improved efficiency, while addressing challenges such as training stability, communication overhead, and load balancing.

Deep LearningMixture of Expertsdynamic routing
0 likes · 7 min read
Why Mixture of Experts (MoE) is Revolutionizing Large AI Models
Baidu Tech Salon
Baidu Tech Salon
Apr 2, 2025 · Artificial Intelligence

PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing

PaddlePaddle 3.0, launched on April 1 2025, introduces five core innovations—including dynamic‑static unified automatic parallelism, a training‑inference integrated PIR, high‑order automatic differentiation for scientific computing, a one‑stage CINN compiler, and heterogeneous multi‑chip adaptation—that dramatically reduce distributed‑training code, boost performance up to four‑fold, and extend the framework to aerospace, automotive, meteorology and life‑science applications while remaining fully compatible with the 2.0 API.

Deep LearningLarge ModelsPaddlePaddle
0 likes · 21 min read
PaddlePaddle Framework 3.0 Released: Five Core Innovations for Large Models and Scientific Computing
Cognitive Technology Team
Cognitive Technology Team
Mar 31, 2025 · Artificial Intelligence

Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching

Recommendation algorithms, rooted in machine learning and deep learning, transform massive user‑generated data into mathematical models that filter and personalize content, covering traditional collaborative filtering, matrix factorization, cosine similarity, and modern deep models such as Wide & Deep and Two‑Tower retrieval, illustrating their evolution and practical applications.

Collaborative FilteringDeep LearningWide&Deep
0 likes · 14 min read
Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching
Cognitive Technology Team
Cognitive Technology Team
Mar 31, 2025 · Artificial Intelligence

Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling

The article explains how Douyin's recommendation system uses machine‑learning and deep‑learning models to predict user actions, assign value weights, and dynamically adjust scores, highlighting both its efficiency in large‑scale content distribution and its inherent limitations compared to human understanding.

AIDeep LearningUser Behavior
0 likes · 7 min read
Understanding Douyin's Recommendation Algorithm: From Behavior Prediction to Value Modeling
JavaEdge
JavaEdge
Mar 27, 2025 · Artificial Intelligence

Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)

This article examines the limitations of current vision‑language and reasoning models, proposes a visual reasoning model (VRM) that can process images and perform deep logical inference, and discusses architecture, training methods, reinforcement‑learning reward designs, and practical challenges.

Artificial IntelligenceDeep LearningLLM
0 likes · 8 min read
Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)
JD Retail Technology
JD Retail Technology
Mar 18, 2025 · Artificial Intelligence

Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems

MaRCA, a multi‑agent reinforcement‑learning framework, allocates compute across JD’s advertising playback chain by jointly estimating user value, resource consumption, and action outcomes while dynamically adjusting to real‑time load, achieving roughly 15 % higher ad revenue without extra compute resources.

AdvertisingCompute SchedulingDeep Learning
0 likes · 18 min read
Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems
JavaEdge
JavaEdge
Mar 15, 2025 · Artificial Intelligence

Boost NLP Model Performance with n-gram Feature Engineering

This article explains why feature engineering is crucial for NLP tasks, introduces n‑gram enhancements, provides Python implementations for generating bi‑gram and higher‑order features, demonstrates dynamic padding for text length standardization, and offers practical deployment tips such as feature dimension control and monitoring.

Deep LearningN-gramNLP
0 likes · 7 min read
Boost NLP Model Performance with n-gram Feature Engineering
AIWalker
AIWalker
Mar 14, 2025 · Artificial Intelligence

Dynamic Tanh Lets He Kaiming and LeCun Drop Transformer Normalization in 9 Lines

Researchers He Kaiming, Yann LeCun and colleagues propose a 9‑line Dynamic Tanh (DyT) layer that replaces LayerNorm/RMSNorm in Transformers, showing comparable or superior accuracy across vision, language, speech and DNA tasks while also reducing inference latency on modern GPUs.

AI researchDeep LearningDynamic Tanh
0 likes · 18 min read
Dynamic Tanh Lets He Kaiming and LeCun Drop Transformer Normalization in 9 Lines
AI Frontier Lectures
AI Frontier Lectures
Mar 14, 2025 · Artificial Intelligence

Do Vision Models Really Need Mamba? A Deep Dive into MambaOut

This article critically examines the MambaOut paper, analyzing whether state‑space‑based Mamba token mixers are necessary for vision tasks, presenting two hypotheses, describing the construction of MambaOut models without SSM, and reporting extensive ImageNet, COCO and ADE20K experiments that reveal when Mamba is beneficial.

Deep LearningMambaState Space Model
0 likes · 17 min read
Do Vision Models Really Need Mamba? A Deep Dive into MambaOut
AIWalker
AIWalker
Mar 10, 2025 · Artificial Intelligence

HSR-Mamba Solves Mamba’s HSISR Issue with Dual Strategies, Beats Prior Methods

HSR-Mamba introduces a contextual spatial‑spectral state‑space model that tackles Mamba's limitations in hyperspectral image super‑resolution through a local partition mechanism and a global spectral rearrangement strategy, achieving significantly higher PSNR, SSIM and SAM scores than existing approaches while using fewer parameters and FLOPs.

Deep LearningDual strategyHSI super-resolution
0 likes · 25 min read
HSR-Mamba Solves Mamba’s HSISR Issue with Dual Strategies, Beats Prior Methods
AIWalker
AIWalker
Mar 8, 2025 · Artificial Intelligence

Trainable HVI Color Space Turns Dark Photos into Cinematic Images – CVPR 2025

The paper introduces a globally first trainable HVI color space and a lightweight CIDNet network that jointly model intensity and chrominance, eliminating color bias and brightness artifacts in low‑light image enhancement and achieving state‑of‑the‑art results on ten benchmark datasets.

CIDNetCVPR 2025Deep Learning
0 likes · 12 min read
Trainable HVI Color Space Turns Dark Photos into Cinematic Images – CVPR 2025
Cognitive Technology Team
Cognitive Technology Team
Mar 6, 2025 · Artificial Intelligence

From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training

This article provides a step‑by‑step tutorial that walks readers through the fundamentals of traditional machine‑learning algorithms, feature‑engineering techniques, model training pipelines, evaluation metrics, and then advances to deep‑learning concepts such as MLPs, activation functions, transformers, and modern recommendation‑system models.

Deep LearningPythonRecommendation Systems
0 likes · 63 min read
From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 6, 2025 · Artificial Intelligence

From Linear Regression to Transformers: Mastering Machine Learning Foundations

This comprehensive guide walks readers through the evolution of machine learning, starting with basic linear models and feature engineering, progressing through logistic regression, decision trees, and deep learning architectures like MLPs, CNNs, RNNs, and transformers, and demonstrates practical implementations with code examples and evaluation metrics.

Deep LearningMachine LearningRecommendation Systems
0 likes · 64 min read
From Linear Regression to Transformers: Mastering Machine Learning Foundations
AntTech
AntTech
Mar 5, 2025 · Artificial Intelligence

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

Pyraformer introduces a pyramidal attention mechanism that captures long-range dependencies in time-series data with linear time and space complexity, achieving state-of-the-art forecasting accuracy on multiple real-world datasets while reducing computational cost, as demonstrated in extensive ICLR-2022 experiments.

Deep LearningICLR 2022Pyraformer
0 likes · 11 min read
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
AIWalker
AIWalker
Mar 1, 2025 · Artificial Intelligence

Lightweight Remote Sensing Backbone LSKNet and Strip R-CNN: Design, Benchmarks, and Open‑Source Release

The NK‑Remote repository introduces LSKNet and Strip R‑CNN, two lightweight yet powerful models for remote‑sensing object detection that dynamically adjust receptive fields and combine square‑and‑strip convolutions, achieving state‑of‑the‑art performance on benchmarks such as DOTA, FAIR1M, HRSC2016, and DIOR.

Deep LearningJDetLSKNet
0 likes · 9 min read
Lightweight Remote Sensing Backbone LSKNet and Strip R-CNN: Design, Benchmarks, and Open‑Source Release
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 27, 2025 · Artificial Intelligence

SAFE: A Lightweight General AI Image Detection Method Achieving 96.7% Accuracy Across 33 Test Subsets

SAFE is a lightweight AI‑image detection framework using only 1.44 M parameters and 2.30 B FLOPs that preserves fine‑grained artifacts through crop‑based preprocessing, invariant augmentations, and high‑frequency wavelet features, achieving an average 96.7 % accuracy across 33 test subsets and strong generalization to unseen GAN and diffusion generators.

AI image detectionDeep LearningGenerative Models
0 likes · 11 min read
SAFE: A Lightweight General AI Image Detection Method Achieving 96.7% Accuracy Across 33 Test Subsets
DataFunTalk
DataFunTalk
Feb 26, 2025 · Artificial Intelligence

Alibaba Cloud's Wanxiang 2.1: Open‑Source Dual‑Version Visual Generation Model with Full‑Scale Capabilities

Wanxiang 2.1, an open‑source visual generation model released by Alibaba Cloud, offers a 140‑billion‑parameter professional version and a 13‑billion‑parameter consumer‑grade version, delivering SOTA performance across multiple benchmarks, supporting diverse video generation tasks, and employing advanced DiT‑based architecture, 3D VAE, and efficient distributed training strategies.

AI modelDeep Learningvisual generation
0 likes · 11 min read
Alibaba Cloud's Wanxiang 2.1: Open‑Source Dual‑Version Visual Generation Model with Full‑Scale Capabilities
AIWalker
AIWalker
Feb 25, 2025 · Artificial Intelligence

Sliding Tile Attention speeds up HunyuanVideo DiT generation 3.5×

Sliding Tile Attention (STA) replaces costly full‑3D attention in video DiT models with a block‑wise sliding‑window scheme, achieving up to 10× attention speedup and a 3.53× end‑to‑end generation boost for HunyuanVideo without quality loss, as demonstrated by extensive benchmarks and kernel analyses.

Deep LearningGPU OptimizationHunyuanVideo
0 likes · 16 min read
Sliding Tile Attention speeds up HunyuanVideo DiT generation 3.5×
JavaEdge
JavaEdge
Feb 24, 2025 · Artificial Intelligence

Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide

This tutorial walks Java developers through building, training, evaluating, and deploying a CIFAR‑10 image classifier using PyTorch, covering data loading, preprocessing, network definition, loss and optimizer setup, GPU acceleration, model saving, and per‑class accuracy analysis.

CIFAR-10Deep LearningGPU
0 likes · 18 min read
Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 24, 2025 · Artificial Intelligence

AIDE: Hybrid Feature Detector for AI‑Generated Image Detection and the Chameleon Benchmark

The paper introduces AIDE, a hybrid AI‑generated image detector that fuses low‑level pixel statistics with high‑level semantic embeddings, and the manually curated Chameleon benchmark of ~26 000 diverse, high‑realism images, showing AIDE surpasses nine state‑of‑the‑art methods by up to 4.6 % while highlighting remaining challenges on this tougher dataset.

AI-generated image detectionDeep Learningbenchmark dataset
0 likes · 14 min read
AIDE: Hybrid Feature Detector for AI‑Generated Image Detection and the Chameleon Benchmark
DaTaobao Tech
DaTaobao Tech
Feb 24, 2025 · Artificial Intelligence

AIGC Video Generation Techniques for E‑commerce: Lip‑Sync, Head/Body Driving, and Business Applications

The article surveys recent AIGC video generation advances for Taobao e‑commerce, detailing lip‑sync models like Wav2Lip and MuseTalk, head‑driven systems such as Hallo and EchoMimic, body‑driven pipelines including AnimateAnyone and Tango, and a four‑stage production workflow that boosts click‑through rates and enables virtual try‑on.

AIGCDeep LearningE‑commerce
0 likes · 21 min read
AIGC Video Generation Techniques for E‑commerce: Lip‑Sync, Head/Body Driving, and Business Applications
DevOps
DevOps
Feb 23, 2025 · Artificial Intelligence

Understanding Reinforcement Learning, RLHF, PPO and GRPO for AI Applications

This article explains how DeepSeek‑R1‑Zero uses group‑relative policy optimization (GRPO) to enhance inference without labeled data, introduces reinforcement learning with human feedback (RLHF) and its components, and compares the PPO and GRPO algorithms, highlighting their suitable engineering scenarios and practical implications for AI applications.

AI model trainingDeep LearningGRPO
0 likes · 15 min read
Understanding Reinforcement Learning, RLHF, PPO and GRPO for AI Applications
JavaEdge
JavaEdge
Feb 23, 2025 · Artificial Intelligence

How Java Developers Can Build Neural Networks with PyTorch: A Step‑by‑Step Guide

This tutorial walks Java developers through the complete workflow of building, training, and evaluating a neural network in PyTorch, covering network definition, data iteration, forward and backward passes, loss calculation, and parameter updates with detailed code examples and Java‑centric analogies.

BackpropagationDeep LearningJava
0 likes · 12 min read
How Java Developers Can Build Neural Networks with PyTorch: A Step‑by‑Step Guide
Subtle Storm
Subtle Storm
Feb 23, 2025 · Artificial Intelligence

What Is Knowledge Distillation and How Does It Compress Deep Models?

Knowledge Distillation transfers the soft output probabilities of a high‑capacity teacher network to a compact student model, enabling significant reductions in parameters and compute while preserving accuracy, and is widely used for model compression, inference acceleration, and transfer learning across resource‑constrained devices.

Deep Learningknowledge distillationmodel compression
0 likes · 7 min read
What Is Knowledge Distillation and How Does It Compress Deep Models?
DeWu Technology
DeWu Technology
Feb 19, 2025 · Artificial Intelligence

Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN

The paper presents a comprehensive multi‑scenario recommendation study introducing three models—SACN, SAINet, and DSWIN—that integrate scene‑aware attention, attribute‑level preferences, and contrastive disentanglement to capture distinct user interests, achieving consistent AUC gains and online CTR improvements across real‑world datasets.

CTR predictionDeep Learningcontrastive learning
0 likes · 43 min read
Scenario-aware Multi-Scenario Recommendation Models: SACN, SAINet, and DSWIN
DataFunTalk
DataFunTalk
Feb 19, 2025 · Artificial Intelligence

Large Models: Concepts, Principles, Classifications and Applications

This report provides a comprehensive overview of large-scale AI models, explaining their definition, massive parameter and data requirements, underlying transformer architecture, classification into language, vision and multimodal models, notable examples such as DeepSeek, and a survey of popular AIGC tools and practical use cases.

AIGC toolsDeep LearningLarge Language Models
0 likes · 9 min read
Large Models: Concepts, Principles, Classifications and Applications
AI Code to Success
AI Code to Success
Feb 19, 2025 · Artificial Intelligence

How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide

This article walks through practical Keras tutorials for image‑based traffic‑sign classification and text‑based sentiment analysis, covering data preparation, preprocessing, model construction, training, evaluation, deployment, and a concise comparison of Keras with TensorFlow and PyTorch.

Deep LearningKerasPyTorch
0 likes · 19 min read
How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide
Python Programming Learning Circle
Python Programming Learning Circle
Feb 18, 2025 · Artificial Intelligence

Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects

This article introduces PyTorch, covering installation on CPU/GPU, basic tensor operations, automatic differentiation, building and training neural networks, data loading with DataLoader, image classification on MNIST, model deployment, and useful tips for accelerating deep‑learning workflows.

Deep LearningGPUMachine Learning
0 likes · 9 min read
Getting Started with PyTorch: Installation, Core Operations, and Practical Deep Learning Projects
AI Code to Success
AI Code to Success
Feb 14, 2025 · Artificial Intelligence

TensorFlow vs PyTorch: Which Deep Learning Framework Wins for Your Projects?

An in‑depth comparison of TensorFlow and PyTorch examines their computation graph models, deployment tools, API ergonomics, community ecosystems, and performance characteristics, helping developers decide which framework best fits industrial production or fast‑paced research scenarios.

AI developmentDeep LearningFramework Comparison
0 likes · 8 min read
TensorFlow vs PyTorch: Which Deep Learning Framework Wins for Your Projects?
AI Code to Success
AI Code to Success
Feb 13, 2025 · Artificial Intelligence

Why PyTorch Is the Go-To Framework for Modern AI Development

This article introduces PyTorch, explains its dynamic computation graph, Python‑centric design, and tensor operations, surveys its major applications in computer vision, natural language processing, and reinforcement learning, and provides a step‑by‑step tutorial for building and training a multilayer perceptron on the MNIST dataset.

Deep LearningDynamic Computation GraphMNIST
0 likes · 11 min read
Why PyTorch Is the Go-To Framework for Modern AI Development
Cognitive Technology Team
Cognitive Technology Team
Feb 12, 2025 · Artificial Intelligence

Introduction to Neural Networks by Professor Li Yongle

In this introductory session, renowned graduate exam instructor Professor Li Yongle provides a clear, beginner-friendly overview of neural networks, covering basic concepts and their relevance within artificial intelligence, including their structure, learning mechanisms, and typical applications in modern AI systems.

AIDeep Learningeducation
0 likes · 1 min read
Introduction to Neural Networks by Professor Li Yongle
AI Code to Success
AI Code to Success
Feb 11, 2025 · Artificial Intelligence

Unlocking TensorFlow: From Basics to Building Your First Linear Regression Model

This article introduces TensorFlow's core concepts—tensors, computational graphs, variables, and sessions—covers its wide range of AI applications from traditional machine learning to deep learning in NLP and computer vision, and provides a step‑by‑step Python tutorial for implementing a simple linear regression model.

AI TutorialDeep LearningLinear Regression
0 likes · 6 min read
Unlocking TensorFlow: From Basics to Building Your First Linear Regression Model
IT Architects Alliance
IT Architects Alliance
Feb 10, 2025 · Artificial Intelligence

DeepSeek Distillation Technology: Principles, Innovations, Performance, and Future Outlook

The article explains DeepSeek's model distillation technique, covering its fundamental knowledge‑transfer principles, unique innovations such as data‑model fusion and task‑specific strategies, impressive benchmark results, practical applications in edge and online inference, existing challenges, and future research directions.

Deep LearningEdge ComputingKnowledge Transfer
0 likes · 15 min read
DeepSeek Distillation Technology: Principles, Innovations, Performance, and Future Outlook
Cognitive Technology Team
Cognitive Technology Team
Feb 9, 2025 · Artificial Intelligence

A Beginner’s Guide to the History and Key Concepts of Deep Learning

From the perceptron’s inception in 1958 to modern Transformer-based models like GPT, this article traces the evolution of deep learning, explaining foundational architectures such as DNNs, CNNs, RNNs, LSTMs, attention mechanisms, and recent innovations like DeepSeek’s MLA, highlighting their principles and impact.

Deep LearningGPTMLA
0 likes · 19 min read
A Beginner’s Guide to the History and Key Concepts of Deep Learning
AIWalker
AIWalker
Feb 9, 2025 · Artificial Intelligence

Douyin’s BDVQAGroup Secures Global Runner‑Up in DXOMARK Image Quality Challenge at CVPR 2024

At CVPR 2024 NTIRE, Douyin’s BDVQAGroup achieved second place worldwide in the DXOMARK portrait quality track using their SampleIQA model, which combines data‑re‑sampling, a Swin‑Transformer backbone, twin‑network ranking loss and content‑aware cropping to outperform existing IQA state‑of‑the‑art methods.

DXOMARKDeep LearningNTIRE2024
0 likes · 10 min read
Douyin’s BDVQAGroup Secures Global Runner‑Up in DXOMARK Image Quality Challenge at CVPR 2024
Cognitive Technology Team
Cognitive Technology Team
Feb 7, 2025 · Artificial Intelligence

Knowledge Distillation: Concepts, Techniques, Applications, and Future Directions

This article explains knowledge distillation—a technique introduced by Geoffrey Hinton that transfers knowledge from large teacher models to compact student models—covering its core concepts, loss functions, various distillation strategies, notable applications in edge computing, federated learning, continual learning, and emerging research directions.

Deep LearningEdge Computingcontinual learning
0 likes · 7 min read
Knowledge Distillation: Concepts, Techniques, Applications, and Future Directions
JavaEdge
JavaEdge
Feb 6, 2025 · Artificial Intelligence

Why Training Transformers Faces an Impossible Triangle of Speed, Performance, and Cost

The article explains the “impossible triangle” in Transformer training, showing how speed, model performance, and computational cost cannot all be optimized simultaneously, and uses analogies and real‑world examples like GPT‑4 to illustrate the necessary trade‑offs.

Artificial IntelligenceDeep LearningPerformance Tradeoff
0 likes · 7 min read
Why Training Transformers Faces an Impossible Triangle of Speed, Performance, and Cost
Architects' Tech Alliance
Architects' Tech Alliance
Feb 4, 2025 · Artificial Intelligence

Why AI Frameworks Are the Backbone of Modern AI – Spotlight on MindSpore

The article explains what AI frameworks are, why they act as the operating system of artificial intelligence, showcases real‑world uses in transportation and finance, and provides an in‑depth analysis of Huawei's MindSpore framework, highlighting its development experience, hardware optimization, deployment flexibility, and enterprise‑grade security features.

AI FrameworkArtificial IntelligenceDeep Learning
0 likes · 7 min read
Why AI Frameworks Are the Backbone of Modern AI – Spotlight on MindSpore
AI Cyberspace
AI Cyberspace
Jan 28, 2025 · Artificial Intelligence

From Biological Neurons to Deep Learning: How MP Models Evolve

This article explains the structure of biological neurons, introduces the McCulloch‑Pitts (MP) mathematical model, shows how manual weight adjustments work, and walks through the development from single‑layer perceptrons to two‑layer networks and modern deep learning techniques, covering activation functions, training algorithms, and practical examples.

BackpropagationDeep LearningMP model
0 likes · 30 min read
From Biological Neurons to Deep Learning: How MP Models Evolve
Python Programming Learning Circle
Python Programming Learning Circle
Jan 14, 2025 · Artificial Intelligence

Age Prediction Using OpenCV and Deep Learning with Python

This tutorial explains how to use OpenCV, pre‑trained deep‑learning models, and Python to automatically detect faces and predict a person's age from static images or real‑time video, covering model selection, project structure, script usage, result analysis, and ways to improve accuracy.

Age EstimationCaffeDeep Learning
0 likes · 18 min read
Age Prediction Using OpenCV and Deep Learning with Python
AIWalker
AIWalker
Jan 13, 2025 · Artificial Intelligence

Multi-View Transformer (MVFormer) Sets New Top‑1 Accuracy Records in Classification, Detection, and Segmentation

The paper proposes MVFormer, a Vision Transformer that combines a Multi‑View Normalization (MVN) module and a Multi‑View Token Mixer (MVTM) to diversify feature learning, achieving state‑of‑the‑art Top‑1 accuracy of 83.4%‑84.6% on ImageNet‑1K and superior performance on COCO detection and ADE20K segmentation while using comparable or fewer parameters and MACs.

Deep LearningMulti-View NormalizationToken Mixer
0 likes · 25 min read
Multi-View Transformer (MVFormer) Sets New Top‑1 Accuracy Records in Classification, Detection, and Segmentation
DeWu Technology
DeWu Technology
Jan 13, 2025 · Artificial Intelligence

Unlock GPU Power: A Hands‑On Triton Guide for Vector Add, Matrix Multiply & RoPE

This article introduces Triton—a Python‑based GPU programming language—covers essential GPU architecture, walks through practical kernels for vector addition, matrix multiplication, and rotary position encoding, compares performance with PyTorch, and provides debugging tips for high‑performance deep‑learning workloads.

CUDADeep LearningGPU programming
0 likes · 22 min read
Unlock GPU Power: A Hands‑On Triton Guide for Vector Add, Matrix Multiply & RoPE
DataFunSummit
DataFunSummit
Jan 13, 2025 · Artificial Intelligence

Deep Learning Approaches for Solving Graph Optimization Problems

This article reviews the use of deep learning, including supervised, reinforcement, and self‑supervised paradigms, to address graph optimization problems such as facility location and balanced graph partitioning, discusses existing research challenges, presents a three‑stage self‑supervised model with graph contrastive pre‑training, and evaluates its performance on synthetic and real‑world datasets.

Deep LearningSelf-supervised Learningcombinatorial optimization
0 likes · 14 min read
Deep Learning Approaches for Solving Graph Optimization Problems
Architects' Tech Alliance
Architects' Tech Alliance
Jan 12, 2025 · Artificial Intelligence

Explore the Full AI Expert Roadmap: From Data Science to Big Data Engineering

The AI Expert Roadmap on GitHub offers a comprehensive, interactive guide covering data‑science fundamentals, machine‑learning algorithms, deep‑learning techniques, data‑engineering pipelines, and big‑data architectures, with linked resources, up‑to‑date references, and practical tool recommendations for aspiring AI professionals.

AIBig DataDeep Learning
0 likes · 6 min read
Explore the Full AI Expert Roadmap: From Data Science to Big Data Engineering
AIWalker
AIWalker
Jan 12, 2025 · Artificial Intelligence

CubeFormer: A Simple Yet Effective Lightweight Image Super‑Resolution Baseline

CubeFormer introduces a novel cube attention mechanism and dual transformer blocks that dramatically improve feature diversity, enabling a lightweight image super‑resolution model to achieve state‑of‑the‑art PSNR and visual detail across multiple benchmarks while keeping parameters low.

Deep Learningcomputer visioncube attention
0 likes · 21 min read
CubeFormer: A Simple Yet Effective Lightweight Image Super‑Resolution Baseline
DataFunSummit
DataFunSummit
Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

Deep LearningE‑commerceReinforcement Learning
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
Python Programming Learning Circle
Python Programming Learning Circle
Jan 3, 2025 · Artificial Intelligence

Visualizing Convolutional Neural Network Features with 40 Lines of Python Code

This article demonstrates how to visualize convolutional features of a VGG‑16 network using only about 40 lines of Python code, explains the underlying concepts, walks through generating patterns by maximizing filter activations, and provides a complete implementation with hooks, loss functions, and multi‑scale optimization.

CNNDeep LearningFeature Visualization
0 likes · 15 min read
Visualizing Convolutional Neural Network Features with 40 Lines of Python Code
JD Retail Technology
JD Retail Technology
Dec 26, 2024 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

MODRL‑TA is a multi‑objective deep reinforcement learning framework that unites independent Q‑learning agents, a cross‑entropy‑based decision‑fusion module, and progressive data‑augmentation to overcome cold‑start and multi‑objective trade‑offs in e‑commerce traffic allocation, delivering up to 18% more impressions, 4% higher CTR and 5% higher CVR in live tests.

Deep LearningE‑commerceReinforcement Learning
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
Model Perspective
Model Perspective
Dec 20, 2024 · Artificial Intelligence

From Monte Carlo to Deep Learning: How Algorithms Evolved to Power AI

This article traces the evolution of algorithms—from the random‑sampling Monte Carlo method through classic machine‑learning models to modern deep‑learning architectures—highlighting how data, computing power, and scientific demand have driven each breakthrough and hinting at future trends like interpretability, AGI, and quantum algorithms.

Artificial IntelligenceDeep LearningMonte Carlo
0 likes · 8 min read
From Monte Carlo to Deep Learning: How Algorithms Evolved to Power AI
Architecture Digest
Architecture Digest
Dec 5, 2024 · Artificial Intelligence

NeurIPS 2024 Best Paper Introduces Visual Autoregressive Modeling (VAR) for Image Generation

A recent NeurIPS 2024 best‑paper award highlights a novel Visual Autoregressive Modeling (VAR) approach that uses multi‑scale token prediction to improve image generation, while the surrounding article also mentions a free book giveaway and a legal dispute involving the paper's author.

Artificial IntelligenceDeep LearningNeurIPS
0 likes · 5 min read
NeurIPS 2024 Best Paper Introduces Visual Autoregressive Modeling (VAR) for Image Generation
AntTech
AntTech
Dec 5, 2024 · Artificial Intelligence

Simplifying Deep Learning: Research Overview by Prof. Yao Quanming

Prof. Yao Quanming presents a comprehensive overview of his research on simplifying deep learning, discussing scaling laws, data, compute and trust bottlenecks, and proposing minimalist approaches in model design, training, and interpretability, with a focus on drug interaction prediction using graph neural networks.

Deep LearningMachine Learningdrug interaction prediction
0 likes · 17 min read
Simplifying Deep Learning: Research Overview by Prof. Yao Quanming
Test Development Learning Exchange
Test Development Learning Exchange
Nov 28, 2024 · Artificial Intelligence

Introduction to Deep Learning with Keras: Building and Training a Simple Neural Network

This tutorial introduces the fundamentals of deep learning, covering neural network basics, Keras fundamentals, and provides a step‑by‑step Python example that loads the Iris dataset, preprocesses data, builds, compiles, trains, evaluates, visualizes, and predicts with a simple neural network model.

AIDeep LearningKeras
0 likes · 7 min read
Introduction to Deep Learning with Keras: Building and Training a Simple Neural Network
DaTaobao Tech
DaTaobao Tech
Nov 25, 2024 · Artificial Intelligence

Open‑Set Object Detection and Visual Grounding: Analysis of YOLO‑World, Grounding DINO, and YOLO11

The article surveys state‑of‑the‑art open‑set object detection and visual‑grounding models—Grounding DINO, YOLO‑World, and the latest YOLO 11—detailing their architectures, training strategies, and experimental results on home‑decoration datasets, showing that open‑set detectors recognize unseen objects while YOLO 11 excels on known categories, and that integrating both approaches yields superior performance, highlighting the expanded potential of detectors for real‑world applications.

Deep LearningGrounding DINOVisual Grounding
0 likes · 15 min read
Open‑Set Object Detection and Visual Grounding: Analysis of YOLO‑World, Grounding DINO, and YOLO11
Baidu Geek Talk
Baidu Geek Talk
Nov 25, 2024 · Artificial Intelligence

PP-ShiTuV2: A General Image Recognition Pipeline in PaddleX

PP‑ShiTuV2, a PaddleX pipeline that integrates subject detection, deep feature encoding, and vector retrieval, delivers 91 % recall@1 on AliProducts, surpasses earlier models by over 20 points, runs efficiently on GPU and CPU, and offers simple installation, quick‑start code, and full fine‑tuning support.

Deep LearningModel deploymentPP-ShiTuV2
0 likes · 8 min read
PP-ShiTuV2: A General Image Recognition Pipeline in PaddleX
DaTaobao Tech
DaTaobao Tech
Nov 13, 2024 · Artificial Intelligence

Understanding Neural Networks and Transformers: Principles, Implementation, and Applications

The article surveys neural networks from basic neuron operations and loss functions through deep architectures to the Transformer model, detailing embeddings, positional encoding, self‑attention, multi‑head attention, residual links, and encoder‑decoder design, and includes PyTorch code examples for linear regression, translation, and fine‑tuning Hugging Face’s MiniRBT for text classification.

AIAttention MechanismDeep Learning
0 likes · 44 min read
Understanding Neural Networks and Transformers: Principles, Implementation, and Applications
Zhuanzhuan Tech
Zhuanzhuan Tech
Nov 6, 2024 · Artificial Intelligence

Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario

This article reviews the necessity, benefits, and practical implementations of multi-task learning in e‑commerce search, detailing model selection, architecture extensions such as ESMM and ESM², and future directions for handling user behavior sequences and multi‑objective optimization.

Deep LearningESMME‑commerce
0 likes · 13 min read
Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario
Tencent Architect
Tencent Architect
Oct 25, 2024 · Artificial Intelligence

How Tencent’s TVQA‑C Algorithm Won the ECCV 2024 Video Quality Challenge

Tencent’s TVQA‑C video quality assessment algorithm clinched first place in the ECCV 2024 AIM Workshop compression video quality track, showcasing a novel model architecture, group‑aware training strategy, and specialized loss functions that will soon power Tencent Cloud’s media processing services.

AIDeep LearningECCV 2024
0 likes · 10 min read
How Tencent’s TVQA‑C Algorithm Won the ECCV 2024 Video Quality Challenge
Tencent Advertising Technology
Tencent Advertising Technology
Oct 17, 2024 · Artificial Intelligence

Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec

This article presents a comprehensive solution for heterogeneous long‑behavior sequence modeling in advertising recommendation, introducing the TIN backbone, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec, along with platform‑level optimizations that enable million‑scale sequences while delivering significant online performance gains.

AdvertisingDeep LearningSequence Modeling
0 likes · 15 min read
Long Sequence Modeling for Advertising Recommendation: TIN, Disentangled Side‑Info TIN, Stacked TIN, and Target‑aware SASRec
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 17, 2024 · Artificial Intelligence

How Meta’s Movie Gen Pushes Text‑to‑Video Generation to New Heights

Meta’s newly released 92‑page Movie Gen paper introduces a multimodal LLM that unifies text‑to‑image, text‑to‑video, personalized video, precise video editing, and audio generation, detailing its dual‑model architecture, training pipeline, temporal auto‑encoder design, scaling strategies, evaluation benchmark, and ablation studies.

Deep LearningEvaluationVideo Generation
0 likes · 34 min read
How Meta’s Movie Gen Pushes Text‑to‑Video Generation to New Heights
iQIYI Technical Product Team
iQIYI Technical Product Team
Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

AdvertisingCTR predictionDeep Learning
0 likes · 9 min read
Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions
21CTO
21CTO
Oct 8, 2024 · Artificial Intelligence

How Baidu Almost Snagged Hinton: The Secret AI Auction That Shaped Deep Learning

This article recounts the little‑known 2012 AI auction in which Baidu, Google, Microsoft and DeepMind vied for Geoffrey Hinton’s fledgling DNNResearch, revealing how the bidding drama propelled deep learning into the mainstream and set the stage for today’s AI arms race.

AI auctionAI historyBaidu
0 likes · 14 min read
How Baidu Almost Snagged Hinton: The Secret AI Auction That Shaped Deep Learning
Bilibili Tech
Bilibili Tech
Oct 8, 2024 · Artificial Intelligence

ICDAR 2024 Historical Map Text Recognition Competition: DNTextSpotter Methodology and Results

The ICDAR 2024 Historical Map Text Recognition competition was won by Bilibili’s DNTextSpotter, a Transformer‑based model built on DeepSolo and ViTAE‑v2 that uses deformable self‑attention, dual‑query decoding and denoising training, combined with mixed‑vocabulary fine‑tuning, advanced loss functions and strict PDQ/PWQ/PCQ metrics to achieve state‑of‑the‑art dense, rotated, arbitrary‑shaped text detection and recognition on historical maps and real‑world multimedia.

DNTextSpotterDeep LearningHistorical Map OCR
0 likes · 17 min read
ICDAR 2024 Historical Map Text Recognition Competition: DNTextSpotter Methodology and Results
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Sep 18, 2024 · Artificial Intelligence

How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code

This article explains why single‑machine resources are insufficient for training ever‑larger language models, introduces the fundamentals of distributed training systems, details various parallel strategies such as data, model, pipeline, and hybrid parallelism, and provides practical PyTorch code and memory‑optimization techniques to accelerate large‑scale model training.

Deep LearningGPULarge Language Models
0 likes · 29 min read
How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Sep 16, 2024 · Artificial Intelligence

Why Transformers Revolutionize AI: From Basics to Advanced Applications

This article explains what AI Transformers are, why they matter, their key components and mechanisms, various applications ranging from language processing to bioinformatics, and how they differ from traditional neural networks, providing a comprehensive overview of Transformer architecture and its impact on modern AI research.

AIDeep LearningNatural Language Processing
0 likes · 20 min read
Why Transformers Revolutionize AI: From Basics to Advanced Applications
Python Programming Learning Circle
Python Programming Learning Circle
Sep 10, 2024 · Artificial Intelligence

Using TorchRL to Implement Multi‑Agent PPO for MARL

This tutorial explains how to set up a multi‑agent reinforcement learning (MARL) environment with VMAS, install required dependencies, configure PPO hyper‑parameters, build policy and critic networks, collect data with TorchRL, and run a training loop to train agents for coordinated navigation tasks.

Deep LearningMulti-Agent Reinforcement LearningPPO
0 likes · 10 min read
Using TorchRL to Implement Multi‑Agent PPO for MARL
Baidu Tech Salon
Baidu Tech Salon
Aug 27, 2024 · Artificial Intelligence

How PaddleX Enables Early Detection of Malignant Skin Tumors with AI Segmentation

This article examines the urgent need for early skin cancer detection in China, outlines the challenges of dermatological imaging, and details a low‑code PaddleX solution that leverages PP‑LiteSeg‑T for data preparation, model training, optimization, and deployment to improve diagnostic accuracy and efficiency.

AIDeep LearningPaddleX
0 likes · 10 min read
How PaddleX Enables Early Detection of Malignant Skin Tumors with AI Segmentation
Bilibili Tech
Bilibili Tech
Aug 27, 2024 · Artificial Intelligence

Multimodal Video Scene Classification for Adaptive Video Processing

The paper presents a multimodal video scene classification system that leverages CLIP‑generated pseudo‑labels and a fine‑tuned image encoder to automatically identify nature, animation/game, and document scenes, enabling more effective adaptive transcoding, intelligent restoration, and quality assessment for user‑generated content on platforms such as Bilibili.

Bilibili multimediaCLIPDeep Learning
0 likes · 17 min read
Multimodal Video Scene Classification for Adaptive Video Processing
Architects' Tech Alliance
Architects' Tech Alliance
Aug 25, 2024 · Industry Insights

Why GPUs May Lose the AI Race: TPU, FPGA, and Future Hardware Trends

While GPUs have driven AI acceleration for years, this article analyzes their architectural constraints, compares emerging alternatives such as Google's TPU and high‑end FPGAs, and explores future application niches like VR/AR, cloud gaming, and military systems where GPUs may still thrive or be replaced.

AI hardwareDeep LearningFPGA
0 likes · 15 min read
Why GPUs May Lose the AI Race: TPU, FPGA, and Future Hardware Trends
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Aug 22, 2024 · Artificial Intelligence

Understanding Faster R-CNN: Architecture, Training, and Experimental Results

This article provides an in‑depth overview of the Faster R‑CNN object detection framework, covering its background, key innovations such as the Region Proposal Network, detailed algorithmic principles, training procedures, experimental results on PASCAL VOC and MS COCO, and a reproducible PyTorch implementation.

Deep LearningFaster R-CNNPyTorch
0 likes · 14 min read
Understanding Faster R-CNN: Architecture, Training, and Experimental Results
Baidu Geek Talk
Baidu Geek Talk
Aug 19, 2024 · Artificial Intelligence

PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains

The PaddlePaddle Neural Network Compiler (CINN) combines a PIR‑based frontend that performs graph‑level optimizations such as constant folding, dead‑code elimination and operator fusion with a backend that applies schedule transformations and auto‑tuning, delivering up to 4× faster RMSNorm kernels and 30‑60% overall speed‑ups for generative AI and scientific‑computing workloads.

CINNDeep LearningGPU
0 likes · 18 min read
PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains
Baidu Geek Talk
Baidu Geek Talk
Aug 14, 2024 · Artificial Intelligence

Sparse Tensor Basics in PaddlePaddle

The article explains how to use PaddlePaddle’s sparse computing features—including basic sparse tensor formats, creation and manipulation of sparse tensors, and building and training sparse neural networks such as a sparse ResNet—to improve memory efficiency and accelerate training on large, zero‑rich datasets.

AICOO FormatCSR Format
0 likes · 22 min read
Sparse Tensor Basics in PaddlePaddle
Open Source Linux
Open Source Linux
Aug 6, 2024 · Artificial Intelligence

What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact

This article explains what artificial intelligence (AI) is, how it differs from traditional programming, outlines its main categories, introduces machine learning, deep learning, neural network models such as CNN, RNN, and Transformer, describes large models and GPT, and discusses AI’s wide‑range applications and societal implications.

AIAI ApplicationsArtificial Intelligence
0 likes · 16 min read
What Is AI? A Beginner’s Guide to Definitions, Types, and Real‑World Impact
160 Technical Team
160 Technical Team
Jul 29, 2024 · Artificial Intelligence

How YOLO Transforms Medical Report Screening and Occlusion Detection

Leveraging the YOLO family of deep‑learning models, this study demonstrates efficient filtering of irrelevant medical images, accurate classification of textual reports, and robust detection of occluding objects, achieving high precision and speed on both CPU and GPU, while outlining training details, performance metrics, and future improvements.

Deep LearningYOLOmedical imaging
0 likes · 17 min read
How YOLO Transforms Medical Report Screening and Occlusion Detection
Baidu Geek Talk
Baidu Geek Talk
Jul 24, 2024 · Artificial Intelligence

AI-Driven Fusion of Peking Opera Characters with Ink-Wash Painting Style Using PaddleGAN

Li Yilin’s AI project blends Peking Opera characters with traditional ink‑wash painting by using PaddleHub for style transfer and PaddleGAN’s First‑Order Motion model for facial motion, then adds music and Wav2Lip lip‑sync, producing videos that modernize Chinese heritage and gauge public cultural awareness.

AIDeep LearningPaddleGAN
0 likes · 9 min read
AI-Driven Fusion of Peking Opera Characters with Ink-Wash Painting Style Using PaddleGAN
Tencent Advertising Technology
Tencent Advertising Technology
Jul 24, 2024 · Artificial Intelligence

Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse

This paper investigates the embedding dimensional collapse problem that hinders scaling of recommendation models and proposes a Multi-Embedding paradigm that learns multiple embeddings per feature with independent expert networks, demonstrating consistent performance gains across major CTR benchmarks and real‑world ad systems.

Artificial IntelligenceCTR predictionDeep Learning
0 likes · 10 min read
Multi-Embedding Paradigm for Scaling Recommendation Models: Mitigating Embedding Dimensional Collapse
JavaEdge
JavaEdge
Jul 22, 2024 · Artificial Intelligence

What Is a Transformer and Why It’s Transforming AI?

This article explains the fundamentals of transformer models, why they outperform earlier neural networks, their core components such as self‑attention and positional encoding, practical use cases across language and biology, and how they differ from RNNs, CNNs, and other architectures.

AIDeep LearningSelf-Attention
0 likes · 20 min read
What Is a Transformer and Why It’s Transforming AI?
NewBeeNLP
NewBeeNLP
Jul 22, 2024 · Artificial Intelligence

How Meta Scales User Modeling for Ads: Inside the SUM Framework

This article examines Meta's SUM (Scaling User Modeling) system, detailing its upstream‑downstream architecture, the SOAP online asynchronous serving platform, production optimizations, and extensive offline and online experiments that demonstrate significant gains in ad personalization performance.

Deep LearningMetaRecommendation Systems
0 likes · 19 min read
How Meta Scales User Modeling for Ads: Inside the SUM Framework
DeWu Technology
DeWu Technology
Jul 19, 2024 · Artificial Intelligence

AI‑Powered Anomaly Detection Algorithms for Observability Metrics

The article explains how AI‑powered anomaly detection—using statistical 3‑sigma/Z-score methods, unsupervised machine‑learning like Isolation Forest, and deep‑learning models such as LSTM, Transformer and Pyraformer—overcomes the limits of threshold‑based monitoring by preprocessing data, reducing false alerts, and delivering high‑precision observability metrics.

AIDeep Learninganomaly detection
0 likes · 13 min read
AI‑Powered Anomaly Detection Algorithms for Observability Metrics
Baidu Geek Talk
Baidu Geek Talk
Jul 17, 2024 · Artificial Intelligence

Tensor Indexing in PaddlePaddle: Concepts, Operations, and Practical Examples

This article explains PaddlePaddle tensor indexing, covering basic slicing, integer and boolean advanced indexing, ellipsis and newaxis usage, assignment in dynamic and static graphs, automatic gradient propagation, and demonstrates practical applications such as semantic segmentation, object detection, and NLP sequence masking.

Advanced IndexingData ProcessingDeep Learning
0 likes · 25 min read
Tensor Indexing in PaddlePaddle: Concepts, Operations, and Practical Examples
Kuaishou Tech
Kuaishou Tech
Jul 16, 2024 · Artificial Intelligence

LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

LivePortrait is an open‑source, controllable portrait video generation framework that transfers facial expressions and poses from a driving video to static or dynamic portraits in real time, leveraging a 69M‑frame mixed video‑image training set, stitching and retargeting modules, and achieving high quality with low latency.

AIDeep LearningVideo Animation
0 likes · 14 min read
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
Ops Development & AI Practice
Ops Development & AI Practice
Jul 6, 2024 · Artificial Intelligence

How Backpropagation Powers Modern Deep Learning: A Deep Dive

This article explains the backpropagation algorithm—its origins, mathematical basis, step‑by‑step workflow, importance for efficient neural network training, and widespread applications in image recognition, natural language processing, and recommendation systems.

BackpropagationDeep LearningMachine Learning
0 likes · 6 min read
How Backpropagation Powers Modern Deep Learning: A Deep Dive
NewBeeNLP
NewBeeNLP
Jul 5, 2024 · Artificial Intelligence

Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance

Meta’s new paper introduces the Wukong model, demonstrating that expanding dense‑layer parameters and computational FLOPs in large‑scale recommendation systems follows a clear scaling law, yielding consistent performance gains across massive internal datasets, with detailed analysis of feature modules, parameter impacts, and experimental results.

CTR modelsDeep LearningMeta
0 likes · 10 min read
Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance
Ops Development & AI Practice
Ops Development & AI Practice
Jul 3, 2024 · Artificial Intelligence

How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview

This article explains the fundamental concepts and architecture of artificial neural networks, describes their learning process, compares them with biological neural systems, and highlights both the similarities and key differences in structure, learning mechanisms, flexibility, and energy efficiency.

Artificial IntelligenceBiological InspirationDeep Learning
0 likes · 7 min read
How Do Artificial Neural Networks Mirror Animal Brains? An In‑Depth Overview
Baidu Tech Salon
Baidu Tech Salon
Jul 3, 2024 · Artificial Intelligence

2024 China College Students AI Innovation Competition Kicks Off

The 2024 China College Students AI Innovation Competition, now in its sixth year and recognized as a national university contest, has opened for global students to create AI large‑model applications using Baidu’s PaddlePaddle, Wenxin and the zero‑code PaddleX pipeline, with a training camp and an August 15 registration deadline.

AI CompetitionAI InnovationDeep Learning
0 likes · 7 min read
2024 China College Students AI Innovation Competition Kicks Off
Kuaishou Tech
Kuaishou Tech
Jul 1, 2024 · Artificial Intelligence

Short-Form Video Quality Assessment Competition at CVPR NTIRE 2024: Dataset, Challenge Overview, and Top Winning Solutions

The CVPR NTIRE 2024 short-form video quality assessment competition introduced the KVQ dataset, attracted over 200 teams, evaluated submissions using SROCC and PLCC metrics, and highlighted the winning approaches of SJTU MMLab, IH‑VQA, and TVQE, showcasing advances in AI‑driven video quality evaluation.

AI CompetitionDeep LearningNTIRE 2024
0 likes · 9 min read
Short-Form Video Quality Assessment Competition at CVPR NTIRE 2024: Dataset, Challenge Overview, and Top Winning Solutions
DaTaobao Tech
DaTaobao Tech
Jul 1, 2024 · Artificial Intelligence

Recent Progress in Vision-Language Models (VLMs)

Over the past year, Vision‑Language Models have surged from early multimodal experiments to competitive open‑source systems rivaling GPT‑4, driven by higher‑resolution processing, richer vision encoders, better projection layers, and larger curated datasets, yet they still face evaluation difficulties, hallucinations, speed limits, and limited multimodal output.

Deep LearningLarge Language ModelsVision-Language Models
0 likes · 24 min read
Recent Progress in Vision-Language Models (VLMs)
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 30, 2024 · Artificial Intelligence

Spatial Attention Mechanism and Its PyTorch Implementation

This article explains the principle of spatial attention in convolutional neural networks, details the underlying algorithmic steps, and provides a complete PyTorch implementation including the attention module, full network architecture, and practical considerations for integrating spatial attention into deep learning models.

CNNDeep LearningNeural Network
0 likes · 10 min read
Spatial Attention Mechanism and Its PyTorch Implementation