Variational Dropout Sparsifies Deep Neural Networks (Molchanov et al. 2017) by Chainer
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Updated
Jun 22, 2017 - Python
Variational Dropout Sparsifies Deep Neural Networks (Molchanov et al. 2017) by Chainer
(ICML-W, 2018) Text to image synthesis, by distilling concepts from multiple captions.
Code for ICML 2019 paper titled "On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning"
Community Regularization of Visually Grounded Dialog https://arxiv.org/abs/1808.04359
AutoLearn, a domain independent regression-based feature learning algorithm.
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction. ICML 2020 Graph Representation Learning and Beyond (GRL+) Workshop
This project contains the code for the paper accepted at NeurIPS 2020 - Robust Meta-learning for Mixed Linear Regression with Small Batches.
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)
ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
⚡️ A framework that investigates the scaling limit of ResNets and compares it to Neural ODEs. Tested on synthetic and standardized datasets. 📈
Code for the paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Official Pytorch implementation of ReBias (Learning De-biased Representations with Biased Representations), ICML 2020
T-Basis: a Compact Representation for Neural Networks
This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.
Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)
Embedding graphs in symmetric spaces
Official code for UnICORNN (ICML 2021)
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
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