FangpingWan

FangpingWan's Stars

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.3k3403383.8k

• jadore801120/attention-is-all-you-need-pytorch

  A PyTorch implementation of the Transformer model in "Attention is All You Need".

  Language:Python8.7k951812k

• KevinMusgrave/pytorch-metric-learning

  The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

  Language:Python5.9k63503657

• tuna/thuthesis

  LaTeX Thesis Template for Tsinghua University

  Language:TeX4.5k886681.1k

• FLHonker/Awesome-Knowledge-Distillation

  Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

  2.5k6112335

• modAL-python/modAL

  A modular active learning framework for Python

  Language:Python2.2k43147323

• google/active-learning

  Language:Python1.1k340205

• isl-org/MultiObjectiveOptimization

  Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

  Language:Python9762043171

• geoopt/geoopt

  Riemannian Adaptive Optimization Methods with pytorch optim

  Language:Python835198777

• asyml/texar-pytorch

  Integrating the Best of TF into PyTorch, for Machine Learning, Natural Language Processing, and Text Generation. This is part of the CASL project: http://casl-project.ai/

  Language:Python74524138117

• songlab-cal/tape

  Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

  Language:Python65022107131

• HazyResearch/hgcn

  Hyperbolic Graph Convolutional Networks in PyTorch.

  Language:Python5862648107

• doufen-org/tofu

  Chrome 扩展，用于备份豆瓣账号的数据，并支持导出 Excel 文档。

  Language:JavaScript54989544

• aimagelab/meshed-memory-transformer

  Meshed-Memory Transformer for Image Captioning. CVPR 2020

  Language:Python5131397136

• churchlab/UniRep

  UniRep model, usage, and examples.

  Language:Python334142796

• mengyao/Complete-Striped-Smith-Waterman-Library

  Language:C2982059112

• brianlan/pytorch-grad-norm

  Pytorch implementation of the GradNorm. GradNorm addresses the problem of balancing multiple losses for multi-task learning by learning adjustable weight coefficients.

  Language:Python2445037

• BlackHC/BatchBALD

  Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

  Language:Python2299552

• EBjerrum/SMILES-enumeration

  SMILES enumeration for QSAR modelling using LSTM recurrent neural networks

  Language:Python21510869

• mariogeiger/hessian

  hessian in pytorch

  Language:Python1856217

• VisionLearningGroup/VisionLearningGroup.github.io

  Language:CSS183171958

• hav4ik/Hydra

  Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.

  Language:Python14611415

• yngvem/group-lasso

  Group Lasso implementation following the scikit-learn API

  Language:Python105123132

• lishuya17/MONN

  MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

  Language:Python9961032

• yunshengtian/DGEMO

  [NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations

  Language:Python995826

• oriondollar/TransVAE

  A Transformer Based VAE Architecture for De Novo Molecular Design

  Language:Jupyter Notebook892623

• ppgaluzio/MOBOpt

  Multi-objective Bayesian optimization

  Language:Python8121723

• marcelampc/aerial_mtl

  PyTorch implementation for multi-task learning with aerial images for the datasets: IEEE Data Fusion Contest 2018 (DFC2018) and ISPRS-Vaihingen.

  Language:Python804710

• timvieira/hypergraphs

  Reference implementations of basic and advanced hypergraph algorithms.

  Language:Python46406

• chao1224/n_gram_graph

  N-Gram Graph: Simple Unsupervised Representation for Graphs, NeurIPS'19 (https://arxiv.org/abs/1806.09206)

  Language:Python38348