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Oral Session
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Tue 6:00
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Oral 4 Track 1: Unsupervised and Self-supervised learning
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Oral Session
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Wed 1:00
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Oral 5 Track 1: Unsupervised and Self-supervised learning & Social Aspects of Machine Learning-
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Oral Session
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Tue 1:00
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Oral 3 Track 4: General Machine Learning & Unsupervised and Self-supervised learning
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Poster
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A theoretical study of inductive biases in contrastive learning
Jeff Z. HaoChen · Tengyu Ma
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Poster
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Mon 7:30
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Amortised Invariance Learning for Contrastive Self-Supervision
Ruchika Chavhan · Jan Stuehmer · Calum Heggan · Mehrdad Yaghoobi · Timothy Hospedales
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Poster
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Wed 7:30
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DAVA: Disentangling Adversarial Variational Autoencoder
Benjamin Estermann · Roger Wattenhofer
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Poster
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Mon 7:30
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Unsupervised visualization of image datasets using contrastive learning
Niklas Böhm · Philipp Berens · Dmitry Kobak
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Poster
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Tue 7:30
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STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
Jaehyun Nam · Jihoon Tack · Kyungmin Lee · Hankook Lee · Jinwoo Shin
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Oral
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Tue 6:20
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STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
Jaehyun Nam · Jihoon Tack · Kyungmin Lee · Hankook Lee · Jinwoo Shin
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Poster
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Information-Theoretic Diffusion
Xianghao Kong · Rob Brekelmans · Greg Ver Steeg
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Poster
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Wed 2:30
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From t-SNE to UMAP with contrastive learning
Sebastian Damrich · Niklas Böhm · Fred A Hamprecht · Dmitry Kobak
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Poster
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Tue 2:30
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Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning
Huiwon Jang · Hankook Lee · Jinwoo Shin
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