ambigeV's repositories
Sustain-AI
A roadmap and comprehensive record for development of sustainable AI in both social wise and environmental wise.
ambigeV.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
bayesian-torch
A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch
CLIP
Contrastive Language-Image Pretraining
BiP
[NeurIPS22] "Advancing Model Pruning via Bi-level Optimization" by Yihua Zhang*, Yuguang Yao*, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, and Sijia Liu
DGEMO
[NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations
gpytorch
A highly efficient implementation of Gaussian Processes in PyTorch
hydra
Code and checkpoints of compressed networks for the paper titled "HYDRA: Pruning Adversarially Robust Neural Networks" (NeurIPS 2020) (https://arxiv.org/abs/2002.10509).
LaMini-LM
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
lm_ner
基于Pytorch的命名实体识别框架，支持LSTM+CRF、Bert+CRF、RoBerta+CRF等框架
meta-learn-tpe
[IJCAI'23] Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
ml-neuman
Official repository of NeuMan: Neural Human Radiance Field from a Single Video (ECCV 2022)
Multi-BioNER
Cross-type Biomedical Named Entity Recognition with Deep Multi-task Learning (Bioinformatics'19)
nerf-pytorch
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
OKDDip-AAAI2020
[AAAI-2020] Official implementation for "Online Knowledge Distillation with Diverse Peers".
OpenPSG
Benchmarking Panoptic Scene Graph Generation (PSG), ECCV'22
ParetoMNMT
Source code for paper "On the Pareto Front of Multilingual Neural Machine Translation"
pytorch-distributed-training
Simple tutorials on Pytorch DDP training
segmentation_models.pytorch
Segmentation models with pretrained backbones. PyTorch.
transferlearning-tutorial
《迁移学习简明手册》LaTex源码
vision
Datasets, Transforms and Models specific to Computer Vision