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Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 9, 2026 · Artificial Intelligence

Time‑o1: Overcoming Time‑Series Forecasting Bottlenecks with a Novel Loss Function

The paper identifies two fundamental issues in time‑series forecasting—label autocorrelation bias and task‑scale explosion caused by the standard TMSE loss—and proposes Time‑o1, a PCA‑based orthogonal label transformation that eliminates bias, reduces optimization complexity, and yields consistent performance gains across multiple models and datasets.

NeurIPS 2025PCATime‑o1
0 likes · 12 min read
Time‑o1: Overcoming Time‑Series Forecasting Bottlenecks with a Novel Loss Function
Qborfy AI
Qborfy AI
Jul 3, 2025 · Artificial Intelligence

Why Loss Functions Matter: From Theory to Real‑World AI Applications

This article explains what loss functions are, outlines their three essential components, categorizes them for regression, classification, and generation tasks, reviews five classic loss functions with their noise resistance and gradient traits, and offers practical guidelines for selecting the right loss for AI models.

AI fundamentalsDeep Learningclassification
0 likes · 4 min read
Why Loss Functions Matter: From Theory to Real‑World AI Applications
AI Frontier Lectures
AI Frontier Lectures
May 11, 2025 · Artificial Intelligence

How VA‑VAE Boosts Diffusion Model Generation: SOTA Results & LightningDiT Insights

This article analyzes the VA‑VAE approach that aligns visual tokenizers with vision foundation models to resolve the reconstruction‑generation trade‑off in latent diffusion models, detailing the VF loss design, adaptive weighting, LightningDiT enhancements, experimental setup, and state‑of‑the‑art ImageNet performance.

LightningDiTVAEloss function
0 likes · 16 min read
How VA‑VAE Boosts Diffusion Model Generation: SOTA Results & LightningDiT Insights
Cognitive Technology Team
Cognitive Technology Team
Apr 9, 2025 · Artificial Intelligence

How Neural Networks Learn: Gradient Descent and Loss Functions

This article explains how neural networks learn by using labeled training data, describing the role of weights, biases, activation functions, and how gradient descent iteratively adjusts parameters to minimize loss, illustrated with the MNIST digit‑recognition example.

Deep LearningMNISTNeural Networks
0 likes · 16 min read
How Neural Networks Learn: Gradient Descent and Loss Functions
IT Services Circle
IT Services Circle
Dec 31, 2024 · Artificial Intelligence

Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide

This article uses a dialogue format to introduce the fundamentals of linear regression, explain how loss functions such as mean squared error quantify prediction errors, and describe gradient descent as an iterative optimization technique for finding the best model parameters, illustrated with simple numeric examples and visual aids.

AI basicsgradient descentlinear regression
0 likes · 13 min read
Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide
Model Perspective
Model Perspective
Sep 10, 2024 · Artificial Intelligence

Why Cross-Entropy Is the Key Loss Function for Classification Models

This article explains how loss functions evaluate model performance, contrasts regression’s mean squared error with classification’s cross‑entropy, describes one‑hot encoding and softmax outputs, and shows why higher predicted probabilities for the correct class yield lower loss, highlighting applications in image, language, and speech tasks.

Softmaxclassificationcross entropy
0 likes · 5 min read
Why Cross-Entropy Is the Key Loss Function for Classification Models
Liangxu Linux
Liangxu Linux
Mar 23, 2024 · Artificial Intelligence

Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks

This article uses a narrative of an AI neuron to explain fundamental concepts of neural networks, including neuron structure, weighted sums, activation functions, loss functions, gradient descent, and learning rate, making complex AI topics accessible to beginners.

AI basicsNeural Networkactivation function
0 likes · 9 min read
Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks
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
Aug 27, 2019 · Artificial Intelligence

How Machines Learn: From Newton’s Second Law to the Core Steps of Supervised Learning

This article illustrates how a machine can rediscover Newton’s second law by treating force and acceleration data as a simple linear regression problem, detailing the three fundamental steps of hypothesis space definition, loss function design, and optimization through calculus or gradient methods.

Newton's lawhypothesis spaceloss function
0 likes · 15 min read
How Machines Learn: From Newton’s Second Law to the Core Steps of Supervised Learning
DataFunTalk
DataFunTalk
Apr 25, 2019 · Artificial Intelligence

Comparison of Classification and Ranking Models in Recommendation Systems

This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.

Recommendation Systemsclassification modelloss function
0 likes · 10 min read
Comparison of Classification and Ranking Models in Recommendation Systems
Meituan Technology Team
Meituan Technology Team
Feb 21, 2019 · Artificial Intelligence

Deep Learning-Based ETA Estimation in Meituan's Delivery System

Meituan’s delivery ETA system progressed from linear regression to DeepFM, enriching user, rider, merchant, and spatiotemporal features, employing an asymmetric loss and business‑rule integration to favor early arrivals, adding a tail‑adjustment term, and is engineered with Spark‑assembled TFRecords, multi‑GPU TensorFlow training, and remote‑served TensorFlow Java inference achieving sub‑5 ms TP99 latency.

ETALong TailTensorFlow Serving
0 likes · 15 min read
Deep Learning-Based ETA Estimation in Meituan's Delivery System