[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
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Updated
Oct 17, 2021 - Python
[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"
PyTorch code for FLD (Feature Likelihood Divergence), FID, KID, Precision, Recall, etc. using DINOv2, InceptionV3, CLIP, etc.
🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights
MirrorMetrics: How to evaluate Stable Diffusion LoRAs. A visual diagnostic tool to detect overfitting, check dataset quality, and fix training settings using InsightFace biometrics.
[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang
ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.
spatial resampling for more robust cross validation in spatial studies
Official Codebase of "A Closer Look at Weakly-Supervised Audio-Visual Source Localization" (NeurIPS 2022)
All exercises for the course Elements of AI - Building AI
Underfitting and overfitting are critical concepts in machine learning, particularly when using Polynomial Regression to model data. Polynomial regression allows a model to learn non-linear relationships by increasing the polynomial degree (e.g. ), making it highly susceptible to both underfitting (too simple) and overfitting (too complex).Solver
策略实盘就绪度证伪器——五门判决,回测能不能实盘,先过五门
Decision Tree classifier from scratch without any machine learning libraries
Statistical overfitting audit for algorithmic trading backtests. Deflated Sharpe Ratio, Probability of Backtest Overfitting, Monte Carlo permutation tests.
Elements of AI: Building AI - Advanced is an online course by Reaktor and University of Helsinki worth 2 ECTS.
Pytorch implementation of the paper: "BMN: Boundary-Matching Network for Temporal Action Proposal Generation", along with three new modules to address overfitting issues found in the baseline model, and their ablation studies.
Detect backtest overfitting & multiple testing: Deflated Sharpe Ratio, PBO (CSCV), purged/embargoed CV, Harvey-Liu haircut. Research/education only, not investment advice.
Pytorch DataLoader wrapper to intentionally mess up, corrupt, shuffle, randomize the input/label correspondence.
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