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Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 18, 2026 · Artificial Intelligence

Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models

This paper evaluates point‑wise, pair‑wise, and list‑wise loss functions for Transformer‑based stock‑return prediction on 110 S&P 500 stocks, showing that Margin loss achieves the highest annual return (16.23%) and Sharpe ratio (0.75), ListNet delivers strong returns with low volatility, and BPR minimizes maximum drawdown, highlighting how loss design critically shapes ranking‑driven portfolio performance.

Loss FunctionsQuantitative TradingStock Ranking
0 likes · 15 min read
Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models
AI Algorithm Path
AI Algorithm Path
Jun 22, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions

This article systematically introduces the most common contrastive learning loss functions—including Contrastive Loss, Triplet Loss, N‑pair Loss, InfoNCE, and Cross‑Entropy—explaining their mathematical formulations, advantages, challenges, and typical applications in visual, textual, and multimodal representation learning.

InfoNCELoss FunctionsVisual-Language Models
0 likes · 10 min read
Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions
Model Perspective
Model Perspective
Aug 25, 2023 · Artificial Intelligence

Understanding Common Loss Functions Across Machine Learning Models

This article explains the purpose of loss functions in machine learning and reviews the specific loss functions used by popular algorithms such as linear regression (MSE), logistic regression (cross‑entropy), decision trees, random forests, SVM (hinge loss), neural networks, and AdaBoost (exponential loss).

AIAlgorithmsLoss Functions
0 likes · 3 min read
Understanding Common Loss Functions Across Machine Learning Models
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

AlgorithmsLoss FunctionsUnsupervised Learning
0 likes · 35 min read
Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2023 · Artificial Intelligence

10 Common Loss Functions and Their Python Implementations

This article explains ten widely used loss functions for regression and classification tasks, describes their mathematical definitions, compares their purposes, and provides complete Python code examples for each, helping readers understand how to select and implement appropriate loss metrics in machine‑learning models.

AILoss Functionsclassification
0 likes · 10 min read
10 Common Loss Functions and Their Python Implementations
Bilibili Tech
Bilibili Tech
Nov 8, 2022 · Artificial Intelligence

Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming

A lightweight real‑time super‑resolution network was created for the 2022 League of Legends S12 World Championship, using pixel‑unshuffle/shuffle, structural re‑parameterization, and a multi‑loss (L1, perceptual, Sobel‑based texture, GAN) training pipeline that upscales 1080p streams to 4K at 75 fps on a V100 GPU, delivering clearer textures and reduced noise while remaining computationally efficient.

Deep LearningLoss Functionsgame streaming
0 likes · 10 min read
Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming
Bilibili Tech
Bilibili Tech
Oct 18, 2022 · Artificial Intelligence

Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming

A real‑time super‑resolution network specially designed for the League of Legends S12 live broadcast upscales 1080p streams to 4K at 75 fps by compressing parameters, employing pixel‑unshuffle/shuffle, structural re‑parameterization, and a multi‑loss (L1, perceptual, Sobel, GAN) training pipeline, delivering markedly sharper textures and lower latency for live game streaming.

Deep LearningLoss Functionsgame streaming
0 likes · 12 min read
Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming
Liulishuo Tech Team
Liulishuo Tech Team
May 20, 2022 · Artificial Intelligence

Multi‑Scale BERT‑Based Automated Essay Scoring: Architecture, Loss Functions, and Experimental Evaluation

This article surveys automated essay scoring (AES), compares handcrafted, deep‑learning, and pre‑trained language‑model approaches, proposes a multi‑scale BERT architecture with document, token, and segment features, introduces three combined loss functions, and demonstrates superior performance on the ASAP dataset and internal tasks.

ASAP datasetBERTLoss Functions
0 likes · 13 min read
Multi‑Scale BERT‑Based Automated Essay Scoring: Architecture, Loss Functions, and Experimental Evaluation
DataFunTalk
DataFunTalk
Apr 5, 2021 · Artificial Intelligence

Summary of Methods and Findings from the NLP Chinese Pre‑training Model Generalization Challenge

The article reviews the Chinese NLP pre‑training model generalization competition, detailing data preprocessing, augmentation, external data usage, model scaling and architecture tweaks, loss functions, learning‑rate and adversarial training strategies, regularization techniques, post‑processing optimizations, and ineffective methods, highlighting their impact on performance metrics.

Loss FunctionsModel OptimizationNLP
0 likes · 15 min read
Summary of Methods and Findings from the NLP Chinese Pre‑training Model Generalization Challenge
MaGe Linux Operations
MaGe Linux Operations
Sep 21, 2018 · Artificial Intelligence

What Classic Diagrams Reveal About Test Error, Overfitting, and Model Selection

The article presents a series of insightful diagrams that illustrate core machine‑learning concepts such as the relationship between training and test error, the dangers of under‑ and over‑fitting, Occam’s razor, feature interactions, discriminative versus generative models, loss functions, least‑squares geometry, and sparsity.

Loss FunctionsModel Selectionbias‑variance
0 likes · 6 min read
What Classic Diagrams Reveal About Test Error, Overfitting, and Model Selection
MaGe Linux Operations
MaGe Linux Operations
Apr 17, 2017 · Artificial Intelligence

Essential Machine Learning Visuals: Test Error, Overfitting, and More

This article presents a curated collection of insightful machine‑learning diagrams that illustrate key concepts such as test versus training error, under‑ and over‑fitting, Occam’s razor, feature interactions, irrelevant features, basis functions, discriminative versus generative models, loss functions, least‑squares geometry, and sparsity.

Loss FunctionsOccam's razorfeature selection
0 likes · 6 min read
Essential Machine Learning Visuals: Test Error, Overfitting, and More