TMLR

Greedy Bayesian Posterior Approximation with Deep Ensembles

Aleksei Tiulpin, Matthew B. Blaschko

Comments Published in the Transactions on Machine Learning Research: https://openreview.net/forum?id=P1DuPJzVTN

Journal ref Transactions on Machine Learning Research, 2022

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, Lucas Beyer

Comments Andreas, Alex, Xiaohua and Lucas contributed equally. We release more than 50'000 ViT models trained under diverse settings on various datasets. Available at https://github.com/google-research/big_vision, https://github.com/google-research/vision_transformer and https://github.com/rwightman/pytorch-image-models TMLR review at https://openreview.net/forum?id=4nPswr1KcP

Journal ref Transactions on Machine Learning Research (05/2022)

Clustering units in neural networks: upstream vs downstream information

Richard D. Lange, David S. Rolnick, Konrad P. Kording

Comments 12 main text pages, 4 main figures, 5 supplemental figures. Will be submitted to TMLR

Journal ref TMLR June (2022)

Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Amini, Zhangyang Wang

Comments Accepted by TMLR 2022

TLDR: Twin Learning for Dimensionality Reduction

Yannis Kalantidis, Carlos Lassance, Jon Almazan, Diane Larlus

Comments Accepted at Transactions on Machine Learning Research (TMLR). Code available at: https://github.com/naver/tldr

Understanding Linearity of Cross-Lingual Word Embedding Mappings

Xutan Peng, Mark Stevenson, Chenghua Lin, Chen Li

Journal ref Transactions on Machine Learning Research (2022)

Boosting Search Engines with Interactive Agents

Leonard Adolphs, Benjamin Boerschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic Kilcher, Sascha Rothe, Pier Giuseppe Sessa, Lierni Sestorain Saralegui

Comments Published in Transactions on Machine Learning Research (06/2022)

Auto-Lambda: Disentangling Dynamic Task Relationships

Shikun Liu, Stephen James, Andrew J. Davison, Edward Johns

Comments Published at TMLR 2022. Project Page: https://shikun.io/projects/auto-lambda Code: https://github.com/lorenmt/auto-lambda