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Meshed-Memory Transformer for Image Captioning. CVPR 2020
A Transformer Based VAE Architecture for De Novo Molecular Design
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/
[NeurIPS 2020] Diversity-Guided Efficient Multi-Objective Optimization With Batch Evaluations
Riemannian Adaptive Optimization Methods with pytorch optim
A modular active learning framework for Python
N-Gram Graph: Simple Unsupervised Representation for Graphs, NeurIPS'19 (https://arxiv.org/abs/1806.09206)
MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins
Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
SMILES enumeration for QSAR modelling using LSTM recurrent neural networks
A PyTorch implementation of the Transformer model in "Attention is All You Need".
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Hyperbolic Graph Convolutional Networks in PyTorch.
Group Lasso implementation following the scikit-learn API
PyTorch implementation for multi-task learning with aerial images for the datasets: IEEE Data Fusion Contest 2018 (DFC2018) and ISPRS-Vaihingen.
Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"
Pytorch implementation of the GradNorm. GradNorm addresses the problem of balancing multiple losses for multi-task learning by learning adjustable weight coefficients.
Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.
Reference implementations of basic and advanced hypergraph algorithms.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习