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06/08/2023
RDumb: A simple approach that questions our progress in continual test-time adaptation
Test-Time Adaptation (TTA) allows to update pretrained models to changin...
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04/01/2022
Learnable latent embeddings for joint behavioral and neural analysis
Mapping behavioral actions to neural activity is a fundamental goal of n...
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10/13/2021
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long stand...
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04/27/2021
Adapting ImageNet-scale models to complex distribution shifts with self-learning
While self-learning methods are an important component in many recent do...
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02/17/2021
Contrastive Learning Inverts the Data Generating Process
Contrastive learning has recently seen tremendous success in self-superv...
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09/01/2020
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Extracting behavioral measurements non-invasively from video is stymied ...
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06/30/2020
Improving robustness against common corruptions by covariate shift adaptation
Today's state-of-the-art machine vision models are vulnerable to image c...
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10/12/2019
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
We propose vq-wav2vec to learn discrete representations of audio segment...
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04/11/2019
wav2vec: Unsupervised Pre-training for Speech Recognition
We explore unsupervised pre-training for speech recognition by learning ...
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08/14/2017