djaniak

djaniak's Stars

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python30.6k4.6k

• rasbt/LLMs-from-scratch

  Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

  Language:Jupyter Notebook21.4k2.2k

• cpark321/uncertainty-deep-learning

  Language:Jupyter Notebook22052

• rgeirhos/Stylized-ImageNet

  Code to create Stylized-ImageNet, a stylized version of standard ImageNet (ICLR 2019 Oral)

  Language:Python49764

• rgeirhos/texture-vs-shape

  Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral)

  Language:R779101

• jason718/awesome-self-supervised-learning

  A curated list of awesome self-supervised methods

  6.1k818

• VectorInstitute/vbll

  Simple (and cheap!) neural network uncertainty estimation

  Language:Python421

• jxzhangjhu/Awesome-LLM-Uncertainty-Reliability-Robustness

  Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

  57342

• ENSTA-U2IS-AI/awesome-uncertainty-deeplearning

  This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

  44943

• VITA-Group/layerGraftedPretraining_ICLR23

  [ICLR 2023] “ Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Better Representations”, Ziyu Jiang, Yinpeng Chen, Mengchen Liu, Dongdong Chen, Xiyang Dai, Lu Yuan, Zicheng Liu, Zhangyang Wang

  Language:Python231

• zjunlp/KnowledgeEditingPapers

  Must-read Papers on Knowledge Editing for Large Language Models.

  70848

• EdisonLeeeee/Awesome-Masked-Autoencoders

  A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

  72551

• Lupin1998/Awesome-MIM

  [Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)

  Language:Python26814

• lightning-uq-box/lightning-uq-box

  Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning

  Language:Python10212

• Lightning-AI/litgpt

  20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.

  Language:Python8.1k821

• mfederici/Multi-View-Information-Bottleneck

  Implementation of Multi-View Information Bottleneck

  Language:Jupyter Notebook11916

• BenediktAlkin/ImageNetSubsetGenerator

  Creates subsets of ImageNet (e.g. ImageNet100)

  Language:Python9

• rapidsai/cuml

  cuML - RAPIDS Machine Learning Library

  Language:C++4k521

• ZIYU-DEEP/Awesome-Information-Bottleneck

  This is a curated list for Information Bottleneck Principle, in memory of Professor Naftali Tishby.

  29236

• lightly-ai/lightly

  A python library for self-supervised learning on images.

  Language:Python2.8k246

• gtegner/mine-pytorch

  Mutual Information Neural Estimation in Pytorch

  Language:Jupyter Notebook27453

• VIDA-NYU/pycalibrate

  pycalibrate is a Python library to visually analyze model calibration in Jupyter Notebooks

  Language:Jupyter Notebook161

• stanislavfort/exploring_the_limits_of_OOD_detection

  Code to replicate the key results from Exploring the Limits of Out-of-Distribution Detection (https://arxiv.org/abs/2106.03004) by Stanislav Fort, Jie Ren, Balaji Lakshminarayanan, published at NeurIPS 2021.

  Language:Jupyter Notebook407

• alinlab/CSI

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

  Language:Python27163

• inspire-group/SSD

  SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

  Language:Python12928

• BGU-CS-VIL/vmdls

  "Variational- and Metric-based Deep Latent Space for Out-of-Distribution Detection" [Dinari and Freifeld, UAI 2022]

  Language:Python10

• jmschrei/pomegranate

  Fast, flexible and easy to use probabilistic modelling in Python.

  Language:Python3.3k590

• Jingkang50/OpenOOD

  Benchmarking Generalized Out-of-Distribution Detection

  Language:Python79296

• PolinaKirichenko/flows_ood

  Language:Jupyter Notebook8511

• rtqichen/beta-tcvae

  code for "Isolating Sources of Disentanglement in Variational Autoencoders".

  Language:Python33567