Jinsol-Lee

Jinsol-Lee's Stars

• tsiege/Tech-Interview-Cheat-Sheet

  Studying for a tech interview sucks. Here's an open source cheat sheet to help

  Language:TypeScript4.2k857963

• JetBrains/lets-plot

  Multiplatform plotting library based on the Grammar of Graphics

  Language:Kotlin1.5k17159049

• fra31/auto-attack

  Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

  Language:Python640968111

• sungyubkim/MINE-Mutual-Information-Neural-Estimation-

  A pytorch implementation of MINE(Mutual Information Neural Estimation)

  Language:Jupyter Notebook3297758

• gtegner/mine-pytorch

  Mutual Information Neural Estimation in Pytorch

  Language:Jupyter Notebook2837654

• alinlab/CSI

  CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

  Language:Python27375462

• deeplearning-wisc/knn-ood

  Code for ICML 2022 paper "Out-of-distribution Detection with Deep Nearest Neighbors"

  Language:Python16922117

• ndb796/Pytorch-Adversarial-Training-CIFAR

  This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.

  Language:Python1504537

• artemyk/ibsgd

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• seominseok0429/label-smoothing-visualization-pytorch

  When Does Label Smoothing Help?_pytorch_implementationimp

  Language:Python1244214

• hlin117/mdlp-discretization

  An implementation of the minimum description length principal expert binning algorithm by Usama Fayyad

  Language:Python101142254

• jkbren/einet

  Uncertainty and causal emergence in complex networks

  Language:Jupyter Notebook1014121

• kfzyqin/Research-Softmax-with-Mutual-Information

  Full implementation of the paper "Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator".

  Language:Python996210

• deeplearning-wisc/large_scale_ood

  MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space

  Language:Python834212

• brain-research/ncp

  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

  Language:Python6211313

• deeplearning-wisc/gradnorm_ood

  On the Importance of Gradients for Detecting Distributional Shifts in the Wild

  Language:Python52287

• liuye6666/adaptive_auto_attack

  Adversarial Robustness, White-box, Adversarial Attack

  Language:Python48304

• adityashrm21/information-theory-deep-learning

  Resources and Implementations (PyTorch) for Information Theoretical concepts in Deep Learning

  Language:Jupyter Notebook41509

• hrayrhar/limit-label-memorization

  Improving generalization by controlling label-noise information in neural network weights.

  Language:Jupyter Notebook39529

• paulbrodersen/entropy_based_binning

  Entropy based binning of discrete variables.

  Language:Python346111

• zeke-xie/artificial-neural-variability-for-deep-learning

  [Neural Computation, MIT Press] The PyTorch Implementation of Variable Optimizers/ Neural Variable Risk Minimization proposed in our Neural Computation paper: Artificial Neural Variability for Deep Learning: On overfitting, Noise Memorization, and Catastrophic Forgetting.

  Language:Python34204

• sphinxteam/dnner

  Deep Neural Networks Entropy from Replicas

  Language:Python31878

• mfederici/dsit

  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"

  Language:Jupyter Notebook24100

• EI-research-group/deep-ei

  Tools for examining the causal structure of artificial neural networks with information theory

  Language:Jupyter Notebook21412

• konstantinklemmer/spate-gan

  SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss

  Language:Jupyter Notebook20220

• mutual-ai/Information-Bottleneck-for-Deep-Learning

  Implementation of MLP (python) and CNN (PyTorch) with Information Plane visualization.

  Language:Jupyter Notebook11207

• KaosEngineer/structured-uncertainty

  Language:Python10301

• marylou-gabrie/learning-synthetic-data

  Package to run learning experiments on synthetic datasets using dnner package for replica entropy computations

  Language:Python10201

• jayjaynandy/maximize-representation-gap

  Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples

  Language:Jupyter Notebook7203

• ivansche/adaptive_estimators

  Code for ICLR 2019 paper "Adaptive Estimators Show Information Compression in Deep Neural Networks" (https://openreview.net/forum?id=SkeZisA5t7)

  Language:Python5103