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05/26/2023
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
We introduce Three Towers (3T), a flexible method to improve the contras...
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03/03/2023
When does Privileged Information Explain Away Label Noise?
Leveraging privileged information (PI), or features available during tra...
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01/30/2023
Massively Scaling Heteroscedastic Classifiers
Heteroscedastic classifiers, which learn a multivariate Gaussian distrib...
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02/18/2022
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Supervised learning datasets often have privileged information, in the f...
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10/06/2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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05/19/2021
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Large scale image classification datasets often contain noisy labels. We...
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09/09/2020
Routing Networks with Co-training for Continual Learning
The core challenge with continual learning is catastrophic forgetting, t...
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06/09/2020
VAEs in the Presence of Missing Data
Real world datasets often contain entries with missing elements e.g. in ...
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03/15/2020
Analysis of Softmax Approximation for Deep Classifiers under Input-Dependent Label Noise
Modelling uncertainty arising from input-dependent label noise is an inc...
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09/18/2019
Memory-Augmented Neural Networks for Machine Translation
Memory-augmented neural networks (MANNs) have been shown to outperform o...
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09/18/2019
Scalable Deep Unsupervised Clustering with Concrete GMVAEs
Discrete random variables are natural components of probabilistic cluste...
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09/27/2018
An Empirical Comparison of Syllabuses for Curriculum Learning
Syllabuses for curriculum learning have been developed on an ad-hoc, per...
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07/25/2018
Deep Contextual Multi-armed Bandits
Contextual multi-armed bandit problems arise frequently in important ind...
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07/23/2018