yangerkun

yangerkun's Stars

• karpathy/LLM101n

  LLM101n: Let's build a Storyteller

  30.8k1.7k

• karpathy/build-nanogpt

  Video+code lecture on building nanoGPT from scratch

  Language:Python3.7k529

• miccunifi/SEARLE

  [ICCV 2023] - Zero-shot Composed Image Retrieval with Textual Inversion

  Language:Python1638

• ispamm/MHyEEG

  Official PyTorch repository for Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals, ICASSPW 2023.

  Language:Python5010

• renhong-zhang/eeg-emotion-recognition-with-vit

  Use Vision Transformer to generate Emotion Recognition using the DEAP dataset and EEG Signals.

  Language:Python566

• Miracle-2001/GNN4EEG

  Language:Python6712

• MedARC-AI/fMRI-reconstruction-NSD

  fMRI-to-image reconstruction on the NSD dataset.

  Language:Jupyter Notebook30944

• CEWu/PTNL

  [ICCV 2023] Official repository of paper titled "Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?"

  Language:Python244

• muzairkhattak/multimodal-prompt-learning

  [CVPR 2023] Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".

  Language:Python68856

• gasteigerjo/dimenet

  DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" (NeurIPS-W 2020)

  Language:Python30462

• yuanqidu/M2Hub

  Language:Python435

• facebookresearch/DiT

  Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

  Language:Python6.6k594

• diff-usion/Awesome-Diffusion-Models

  A collection of resources and papers on Diffusion Models

  Language:HTML11.3k952

• yangerkun/IJCAI2018_SSDH

  Semantic Structure-based Unsupervised Deep Hashing IJCAI2018

  Language:Python4415

• jd-opensource/TeD-Q

  TeD-Q (Tensor-network enhanced Distributed Quantum) is a tensor network enhanced distributed hybrid quantum machine learning framework.

  Language:Python944

• nazmul-karim170/UNICON

  [CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"

  Language:Python6014

• GeorgeCazenavette/mtt-distillation

  Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

  Language:Python41054

• weijiaheng/Advances-in-Label-Noise-Learning

  A curated (most recent) list of resources for Learning with Noisy Labels

  69959

• Kthyeon/FINE_official

  NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

  Language:Python3914

• locuslab/fast_adversarial

  [ICLR 2020] A repository for extremely fast adversarial training using FGSM

  Language:Python44091

• emeryberger/CSrankings

  A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

  Language:Python2.8k3.3k

• RunxinXu/ChildTuning

  Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

  Language:Python566

• facebookresearch/luckmatters

  Understanding Training Dynamics of Deep ReLU Networks

  Language:Python28431

• maple-research-lab/AdCo

  AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

  Language:Python16219

• cszn/BSRGAN

  Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!

  Language:Python1.2k183

• zhuxinqimac/PS-SC

  Code for CVPR2021 paper 'Where and What? Examining Interpretable Disentangled Representations'.

  Language:Python445

• open-mmlab/mmdetection

  OpenMMLab Detection Toolbox and Benchmark

  Language:Python29.9k9.5k

• yangerkun/vimrc

  The ultimate Vim configuration: vimrc

  1

• yangerkun/Classification-with-noisy-labels-by-importance-reweighting

  TPAMI: Classification with noisy labels by importance reweighting.

  1

• yangerkun/SML-Group

  The code that sml group often uses

  1