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08/25/2023
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
Real-world time series data that commonly reflect sequential human behav...
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05/26/2023
LANISTR: Multimodal Learning from Structured and Unstructured Data
Multimodal large-scale pretraining has shown impressive performance gain...
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04/07/2023
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Selective prediction aims to learn a reliable model that abstains from m...
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11/28/2022
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations
Spurious correlations, or correlations that change across domains where ...
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06/15/2022
Test-Time Adaptation for Visual Document Understanding
Self-supervised pretraining has been able to produce transferable repres...
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04/21/2022
Contrastive Test-Time Adaptation
Test-time adaptation is a special setting of unsupervised domain adaptat...
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09/02/2021
On-target Adaptation
Domain adaptation seeks to mitigate the shift between training on the so...
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08/20/2021
Region-level Active Learning for Cluttered Scenes
Active learning for object detection is conventionally achieved by apply...
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07/07/2021
Predicting with Confidence on Unseen Distributions
Recent work has shown that the performance of machine learning models ca...
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03/23/2021
Self-Supervised Pretraining Improves Self-Supervised Pretraining
While self-supervised pretraining has proven beneficial for many compute...
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12/18/2020
Minimax Active Learning
Active learning aims to develop label-efficient algorithms by querying t...
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10/04/2020
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting
The goal of continual learning (CL) is to learn a sequence of tasks with...
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03/21/2020
Adversarial Continual Learning
Continual learning aims to learn new tasks without forgetting previously...
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09/23/2019
WiCV 2019: The Sixth Women In Computer Vision Workshop
In this paper we present the Women in Computer Vision Workshop - WiCV 20...
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06/06/2019
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Continual learning aims to learn new tasks without forgetting previously...
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03/31/2019
Variational Adversarial Active Learning
Active learning aims to develop label-efficient algorithms by sampling t...
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12/05/2018
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal ma...
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07/19/2018
Compositional GAN: Learning Conditional Image Composition
Generative Adversarial Networks (GANs) can produce images of surprising ...
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10/16/2017
