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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 EstimationCaffeComputer Vision
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.

Computer VisionDeep LearningMulti-View Normalization
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 Learningcombinatorial optimizationexperimental evaluation
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 DataData Science
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.

Computer VisionDeep Learningcube 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 Learningcross-entropy methode‑commerce
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‑commercemulti-objective
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.

Deep LearningMonte Carloalgorithm evolution
0 likes · 8 min read
From Monte Carlo to Deep Learning: How Algorithms Evolved to Power AI
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 Learningdrug interaction predictionmachine learning
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.

Computer VisionDeep LearningGrounding DINO
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.

Computer VisionDeep LearningModel Deployment
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 LearningESMMModel architecture
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 LearningPerformance Optimization
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 LearningModel ScalingVideo 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 LearningEvaluation Metrics
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 LearningGPUParallelism
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 LearningSelf-Attention
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 LearningPPOTorchRL
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 multimediaCLIPComputer Vision
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.

Computer VisionDeep LearningFaster R-CNN
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 applicationsDeep Learning
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.

AIComputer VisionDeep Learning
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.

CTR predictionDeep Learningartificial intelligence
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 IndexingDeep LearningGradient Propagation
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.

AIComputer VisionDeep Learning
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 LearningNeural Networks
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.

Biological InspirationDeep LearningNeural Networks
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 InnovationAI competitionDeep 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 competitionComputer VisionDataset
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.

Computer VisionDeep LearningVision-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
DataFunTalk
DataFunTalk
Jun 29, 2024 · Artificial Intelligence

Document Intelligence in the Financial Sector: Technologies, Challenges, and Future Directions

This presentation reviews the technical scope of document intelligence, its specific applications and challenges in finance, recent advances in document analysis, recognition, and understanding, and outlines future research directions for large‑model and multimodal solutions in processing complex financial documents.

Deep LearningDocument AIlarge models
0 likes · 28 min read
Document Intelligence in the Financial Sector: Technologies, Challenges, and Future Directions
JD Tech
JD Tech
Jun 28, 2024 · Artificial Intelligence

An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI

This article provides a comprehensive introduction to large language models, covering their historical development, core architecture, training process, prompt engineering techniques, Retrieval‑Augmented Generation, agent frameworks, multimodal capabilities, safety challenges, and future research directions.

AI SafetyAI agentsDeep Learning
0 likes · 22 min read
An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI
Ops Development & AI Practice
Ops Development & AI Practice
Jun 22, 2024 · Artificial Intelligence

Why Transformers Revolutionized AI: From NLP to Vision and Speech

Transformers, introduced in 2017, have reshaped neural networks by leveraging attention mechanisms to outperform RNNs and CNNs across NLP, computer vision, and speech tasks, offering parallel processing, long‑range dependency capture, and versatile applications such as translation, text generation, image classification, and speech recognition.

Attention MechanismComputer VisionDeep Learning
0 likes · 6 min read
Why Transformers Revolutionized AI: From NLP to Vision and Speech
DataFunTalk
DataFunTalk
Jun 20, 2024 · Artificial Intelligence

User Profiling Algorithms: From Ontology‑Based Methods to Deep Learning and Large Model Integration

This article provides a comprehensive overview of user profiling algorithms, covering the evolution from ontology‑based traditional methods to modern deep‑learning approaches, including structured label prediction, representation learning, active learning, and large‑model integration, while discussing challenges, practical applications, and future research directions.

Deep LearningOntologyactive learning
0 likes · 26 min read
User Profiling Algorithms: From Ontology‑Based Methods to Deep Learning and Large Model Integration
AntTech
AntTech
Jun 18, 2024 · Artificial Intelligence

Ant Group’s 24 Papers Featured at CVPR2024: Topics and Abstracts

The IEEE CVPR2024 conference in Seattle accepted 2,719 papers out of 11,532 submissions, and Ant Group contributed 24 papers covering computer vision, deep learning, digital humans, large models, multimodal remote sensing, vision‑language distillation, federated incremental learning, model‑stealing defense, and more, with one highlighted as a highlight.

Ant GroupCVPR2024Computer Vision
0 likes · 17 min read
Ant Group’s 24 Papers Featured at CVPR2024: Topics and Abstracts
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Jun 17, 2024 · Artificial Intelligence

Xiaohongshu Audio-Video Architecture Team Wins Top Awards in CVPR NTIRE 2024 Challenges

Xiaohongshu’s audio‑video architecture team secured second place in the RAIM challenge and first in the S‑UGC VQA challenge at CVPR NTIRE 2024 by improving generative image restoration with SUPIR, DeSRA and a Fusion model, and enhancing video quality assessment using LIQE, Q‑Align and FAST‑VQA, then deploying these methods for live‑stream denoising, intelligent transcoding and cloud‑based super‑resolution, achieving high PLCC/SROCC scores and up to 33 % bandwidth savings.

AICVPR NTIRE 2024Deep Learning
0 likes · 25 min read
Xiaohongshu Audio-Video Architecture Team Wins Top Awards in CVPR NTIRE 2024 Challenges
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 16, 2024 · Artificial Intelligence

HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline

This article provides a detailed, English-language walkthrough of the HRNet source code, covering how the COCO keypoint dataset is built, the online data‑augmentation techniques applied during training, and the end‑to‑end training and inference procedures for human pose estimation.

Computer VisionDeep LearningHRNet
0 likes · 36 min read
HRNet Source Code Walkthrough: Keypoint Dataset Construction, Online Data Augmentation, and Training Pipeline
Baidu Tech Salon
Baidu Tech Salon
Jun 14, 2024 · Artificial Intelligence

Why Large Models Signal the Dawn of General AI: Insights from Baidu’s CTO

In a keynote at the 2024 Beijing Zhiyuan Conference, Baidu’s CTO Wang Haifeng explained how large‑model universality and comprehensive capabilities are driving artificial general intelligence forward, highlighting scale laws, multimodal advances, agent technologies, and the industrial‑scale production of AI.

AI industrializationAI trendsDeep Learning
0 likes · 7 min read
Why Large Models Signal the Dawn of General AI: Insights from Baidu’s CTO
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jun 12, 2024 · Artificial Intelligence

A Simple Introduction to the Transformer Model

This article provides a comprehensive, beginner-friendly explanation of the Transformer architecture, covering its encoder‑decoder structure, self‑attention, multi‑head attention, positional encoding, residual connections, decoding process, final linear and softmax layers, and training considerations, illustrated with numerous diagrams and code snippets.

Deep LearningNeural NetworksSelf-Attention
0 likes · 24 min read
A Simple Introduction to the Transformer Model
21CTO
21CTO
Jun 2, 2024 · Artificial Intelligence

Geoff Hinton on Scaling Laws, Multimodal AI, and the Future of Intelligence

In a candid interview, Geoff Hinton reflects on his AI journey—from early disappointments in physiology and philosophy to breakthroughs in neural networks, scaling laws, multimodal learning, fast‑weight concepts, and the ethical challenges shaping the future of artificial intelligence.

AI ethicsDeep LearningGeoff Hinton
0 likes · 25 min read
Geoff Hinton on Scaling Laws, Multimodal AI, and the Future of Intelligence
Liangxu Linux
Liangxu Linux
May 26, 2024 · Artificial Intelligence

Can Palette-Based Recoloring Transform Pokémon Images Without Neural Networks?

This article presents a mathematically modeled algorithm that extracts color palettes from any Pokémon image and applies them to another, optimizing the swap via deep‑feature distance and dense color‑transform space, demonstrating superior visual results and subjective evaluations compared to traditional hue‑shift and other recoloring methods.

Computer VisionDeep Learningcolor transfer
0 likes · 14 min read
Can Palette-Based Recoloring Transform Pokémon Images Without Neural Networks?
DataFunSummit
DataFunSummit
May 25, 2024 · Artificial Intelligence

Debiased Deep Learning and Double Machine Learning for Multi‑Experiment Causal Inference

This article presents a novel approach that combines debiased deep learning with double machine learning to estimate and infer average treatment effects across multiple simultaneous online experiments, detailing problem definition, a semi‑parametric theoretical framework, and extensive field‑experiment validation on a large video‑platform dataset.

ATE estimationDeep Learningcausal inference
0 likes · 11 min read
Debiased Deep Learning and Double Machine Learning for Multi‑Experiment Causal Inference
Baidu Tech Salon
Baidu Tech Salon
May 24, 2024 · Artificial Intelligence

HelixDock: A Large-Scale Pretrained Full-Atom Diffusion Model for Protein–Small Molecule Docking

HelixDock, a full‑atom diffusion model pretrained on a billion‑scale simulated docking dataset covering ~200,000 protein targets, delivers state‑of‑the‑art docking accuracy—85.6% success on PoseBusters and strong generalization on cross‑docking benchmarks—showing that massive data and model scaling dramatically improve AI‑driven drug discovery, and its code and data are fully open‑source.

AI for drug discoveryDeep LearningHelixDock
0 likes · 6 min read
HelixDock: A Large-Scale Pretrained Full-Atom Diffusion Model for Protein–Small Molecule Docking
Huolala Tech
Huolala Tech
May 23, 2024 · Artificial Intelligence

How to Detect and Remove Moiré Patterns with AI and Diffusion Models

This article explains the nature of moiré patterns in digital imaging, reviews manual mitigation techniques, introduces direct and indirect AI‑based recognition methods—including traditional feature extraction and deep‑learning models such as CNNs and diffusion frameworks—and details practical applications and evaluation metrics used by Huolala.

AIComputer VisionDeep Learning
0 likes · 17 min read
How to Detect and Remove Moiré Patterns with AI and Diffusion Models
Open Source Linux
Open Source Linux
May 22, 2024 · Artificial Intelligence

Why GPUs Are the Powerhouse Behind Modern AI: A Deep Dive

This article explains how GPUs, with their parallel architecture and extensive software ecosystem, have become essential for accelerating AI training and inference, outperforming CPUs and shaping the future of artificial intelligence across various industries.

Deep LearningGPUHardware acceleration
0 likes · 10 min read
Why GPUs Are the Powerhouse Behind Modern AI: A Deep Dive
Architects Research Society
Architects Research Society
May 21, 2024 · Artificial Intelligence

27 Essential AI Papers Recommended by Ilya Sutskever for John Carmack

Ilya Sutskever, former OpenAI chief scientist, shared a curated list of 27 seminal AI research papers—including the Annotated Transformer, Attention Is All You Need, and Deep Residual Learning—with links, claiming mastering them covers roughly 90% of today’s essential artificial‑intelligence knowledge.

AIDeep LearningNeural Networks
0 likes · 7 min read
27 Essential AI Papers Recommended by Ilya Sutskever for John Carmack
Baidu Tech Salon
Baidu Tech Salon
May 20, 2024 · Artificial Intelligence

HelixFold-Multimer: High‑Performance Antigen‑Antibody and Peptide‑Protein Complex Structure Prediction

HelixFold‑Multimer, a new Baidu PaddleHelix model, outperforms AlphaFold 3 on antigen‑antibody and peptide‑protein complex predictions, achieving mean DockQ scores of 0.41 and 0.38 respectively and success rates up to 77 % when epitope data are used, and is already deployed in large‑molecule drug pipelines.

Deep LearningHelixFold-Multimerantibody
0 likes · 7 min read
HelixFold-Multimer: High‑Performance Antigen‑Antibody and Peptide‑Protein Complex Structure Prediction
JD Tech
JD Tech
May 17, 2024 · Artificial Intelligence

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

The article details how JD's advertising retrieval platform tackles the core challenge of balancing limited compute resources with massive data by optimizing compute allocation, improving model scoring efficiency, and enhancing iteration speed through distributed execution graphs, adaptive algorithms, and platform‑level infrastructure improvements.

ANNAdvertisingDeep Learning
0 likes · 24 min read
Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency
Architects' Tech Alliance
Architects' Tech Alliance
May 14, 2024 · Artificial Intelligence

Why GPUs Are Essential for Modern Artificial Intelligence and How They Compare with CPUs, ASICs, and FPGAs

This article explains the pivotal role of GPUs in today’s generative AI era, describes their architecture and applications, compares them with CPUs, ASICs, and FPGAs, and offers guidance on selecting the right processor for AI workloads while also noting related reference resources.

Deep LearningGPUHardware
0 likes · 12 min read
Why GPUs Are Essential for Modern Artificial Intelligence and How They Compare with CPUs, ASICs, and FPGAs
Architect's Guide
Architect's Guide
May 13, 2024 · Artificial Intelligence

Understanding the Core Principles of Transformer Architecture

This article explains how Transformer models work by detailing the encoder‑decoder structure, self‑attention, multi‑head attention, positional encoding, and feed‑forward networks, and shows their applications in machine translation, recommendation systems, and large language models.

AIAttention MechanismDeep Learning
0 likes · 11 min read
Understanding the Core Principles of Transformer Architecture
JD Cloud Developers
JD Cloud Developers
Apr 30, 2024 · Artificial Intelligence

Build a Handwritten Digit Recognizer in Java with TensorFlow

This article walks through the complete process of creating, training, evaluating, saving, and loading a MNIST handwritten digit recognition model using TensorFlow in Java, comparing it with the equivalent Python implementation and covering required knowledge, environment setup, and code details.

Deep LearningJavaMNIST
0 likes · 34 min read
Build a Handwritten Digit Recognizer in Java with TensorFlow
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 24, 2024 · Artificial Intelligence

Training MNIST with Burn on wgpu: From PyTorch to Rust Backend

This tutorial demonstrates how to train a MNIST digit‑recognition model using the Rust‑based Burn framework on top of the cross‑platform wgpu API, covering model export from PyTorch to ONNX, code generation, data loading, training loops, and performance comparison across CPU, GPU, and other backends.

BurnDeep LearningGPU
0 likes · 13 min read
Training MNIST with Burn on wgpu: From PyTorch to Rust Backend
DaTaobao Tech
DaTaobao Tech
Apr 22, 2024 · Artificial Intelligence

Neural Networks and Deep Learning: Principles and MNIST Example

The article reviews recent generative‑AI breakthroughs such as GPT‑5 and AI software engineers, explains that AI systems are deterministic rather than black boxes, and then teaches neural‑network fundamentals—including activation functions, back‑propagation, and a hands‑on MNIST digit‑recognition example with discussion of overfitting and regularization.

Deep LearningMNISTNeural Networks
0 likes · 17 min read
Neural Networks and Deep Learning: Principles and MNIST Example
Top Architect
Top Architect
Apr 18, 2024 · Artificial Intelligence

Understanding Transformers: Architecture, Attention Mechanism, Training and Inference

This article provides a comprehensive overview of Transformer models, covering their attention-based architecture, encoder-decoder structure, training procedures including teacher forcing, inference workflow, advantages over RNNs, and various applications in natural language processing such as translation, summarization, and classification.

Attention MechanismDeep LearningInference
0 likes · 11 min read
Understanding Transformers: Architecture, Attention Mechanism, Training and Inference
NewBeeNLP
NewBeeNLP
Apr 16, 2024 · Artificial Intelligence

Demystifying the Transformer: Step‑by‑Step PaddlePaddle Implementation

This article provides a comprehensive, code‑rich walkthrough of the Transformer architecture using PaddlePaddle, covering the encoder and decoder components, residual connections, layer normalization, feed‑forward networks, scaled dot‑product and multi‑head attention, and shows how to assemble the full model with training and inference functions.

Attention MechanismDecoderDeep Learning
0 likes · 17 min read
Demystifying the Transformer: Step‑by‑Step PaddlePaddle Implementation
DataFunSummit
DataFunSummit
Apr 15, 2024 · Artificial Intelligence

Deep Learning Practices for Internet Real‑Estate Recommendation at 58.com

This article details the end‑to‑end deep‑learning pipeline used by 58.com for real‑estate recommendation, covering business background, a six‑layer architecture, vector‑based recall, various embedding and ranking models, multi‑task and multi‑scenario optimization techniques, and future directions for large‑model integration.

Deep LearningFAISSmulti-task learning
0 likes · 19 min read
Deep Learning Practices for Internet Real‑Estate Recommendation at 58.com
AI Algorithm Path
AI Algorithm Path
Apr 5, 2024 · Artificial Intelligence

Master CNN, RNN, GAN, and Transformer Architectures in One Guide

This article provides a friendly, step‑by‑step overview of five core deep‑learning architectures—CNN, RNN, GAN, Transformers, and encoder‑decoder—explaining their structures, key components, and typical use cases in image and natural‑language processing.

CNNDeep LearningEncoder-Decoder
0 likes · 12 min read
Master CNN, RNN, GAN, and Transformer Architectures in One Guide
DataFunTalk
DataFunTalk
Apr 2, 2024 · Artificial Intelligence

User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions

This article provides a comprehensive overview of user portrait algorithms, covering their historical development, ontology‑based traditional approaches, deep‑learning enhancements, representation‑learning techniques such as lookalike, active‑learning driven iteration, and the integration of large‑model world knowledge, while also discussing current challenges and future research directions.

Deep LearningOntologyRecommendation Systems
0 likes · 26 min read
User Portrait Algorithms: From Ontology‑Based Methods to Deep Learning and Future Directions
Architect
Architect
Mar 28, 2024 · Artificial Intelligence

Understanding OpenAI's Sora Video Generation Model: Architecture, Workflow, and Core Technologies

This article explains OpenAI's Sora video generation model, detailing its latent diffusion foundation, video compression network, spacetime patch representation, Diffusion Transformer processing, and decoding pipeline, while also reviewing related Stable Diffusion and Transformer concepts that enable high‑quality text‑to‑video synthesis.

AIDeep LearningLatent Diffusion
0 likes · 17 min read
Understanding OpenAI's Sora Video Generation Model: Architecture, Workflow, and Core Technologies
Test Development Learning Exchange
Test Development Learning Exchange
Mar 27, 2024 · Artificial Intelligence

Introduction to PyTorch and Example CNN Training on CIFAR-10

This article introduces PyTorch as a leading open‑source deep‑learning framework, outlines its key components such as dynamic computation graphs, tensors, autograd, modules, optimizers, data loading, distributed training and TorchScript, and provides a complete Python example that defines a simple CNN and trains it on the CIFAR‑10 dataset.

CNNDeep LearningPyTorch
0 likes · 8 min read
Introduction to PyTorch and Example CNN Training on CIFAR-10
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 15, 2024 · Artificial Intelligence

Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning

Researchers from Alibaba Cloud AI and Zhejiang University present AMFormer, a Transformer‑based model that incorporates arithmetic feature interaction, demonstrating superior fine‑grained modeling, sample efficiency, and generalization on synthetic and real‑world tabular datasets, establishing a new state‑of‑the‑art in deep tabular learning.

AMFormerDeep LearningTransformer
0 likes · 12 min read
Why Arithmetic Feature Interaction Is Key to Deep Tabular Learning
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Mar 12, 2024 · Artificial Intelligence

AAAI‑2024 Highlights: Alibaba Cloud’s Deep Tabular Learning & Multi‑Modal Fusion

Alibaba Cloud’s AI platform PAI showcased four cutting‑edge papers at AAAI‑2024—introducing AMFormer for deep tabular learning via arithmetic feature interaction, MuLTI for efficient video‑language understanding, M2SD for few‑shot class‑incremental learning, and M2Doc for multi‑modal document layout analysis—demonstrating the platform’s growing impact on artificial‑intelligence research.

Deep LearningFew‑Shot LearningMultimodal AI
0 likes · 9 min read
AAAI‑2024 Highlights: Alibaba Cloud’s Deep Tabular Learning & Multi‑Modal Fusion
Sohu Tech Products
Sohu Tech Products
Mar 6, 2024 · Artificial Intelligence

Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models

This article provides an in‑depth overview of regression prediction, covering linear models like OLS, Lasso, Ridge, and Bayesian approaches, as well as non‑linear techniques such as tree ensembles, SVR, KNN, neural networks, and advanced deep learning frameworks for tabular data.

Deep Learninggradient boostinglinear models
0 likes · 13 min read
Mastering Regression: A Comprehensive Guide to Linear and Non‑Linear Models
NewBeeNLP
NewBeeNLP
Mar 4, 2024 · Artificial Intelligence

A Curated Tour of Mamba Papers: 25 Cutting‑Edge State‑Space Model Innovations

This article presents a GitHub‑hosted collection of 25 recent research papers on Mamba and its variants, summarizing each work’s core contributions across sequence modeling, vision, medical imaging, graph analysis, and multimodal tasks, and highlighting their performance gains over prior methods.

Computer VisionDeep LearningMamba
0 likes · 13 min read
A Curated Tour of Mamba Papers: 25 Cutting‑Edge State‑Space Model Innovations
Bilibili Tech
Bilibili Tech
Mar 1, 2024 · Artificial Intelligence

Bilibili's Self-Developed Video Super-Resolution Algorithm: Background, Optimization Directions, and Implementation Details

Bilibili’s self‑supervised video super‑resolution system upgrades low‑resolution streams to 4K by using three parallel degradation‑branch networks—texture‑enhancing, line‑recovering, and noise‑removing—tailored to anime, game, and real‑world content, delivering sharper edges, finer textures, and measurable quality gains across its online playback pipeline.

AIBilibiliDeep Learning
0 likes · 16 min read
Bilibili's Self-Developed Video Super-Resolution Algorithm: Background, Optimization Directions, and Implementation Details
DataFunTalk
DataFunTalk
Feb 24, 2024 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.

Deep Learningcausal discoverycausal inference
0 likes · 11 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
Ximalaya Technology Team
Ximalaya Technology Team
Feb 20, 2024 · Artificial Intelligence

Optimization of Deep Learning-Based CTR Models in Advertising

This report presents recent advances in optimizing deep learning click‑through‑rate models for advertising, including improved embedding mechanisms, novel feature‑interaction and architecture designs such as attention‑based behavior sequencing, multi‑tower and Mixture‑of‑Experts networks, dynamic ID handling, hourly updates, incremental training, and outlines future multi‑modal and embedding‑importance research.

CTR modelDeep LearningEmbedding Techniques
0 likes · 13 min read
Optimization of Deep Learning-Based CTR Models in Advertising