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02/09/2023
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Sim2Real domain adaptation (DA) research focuses on the constrained sett...
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10/05/2022
The Calibration Generalization Gap
Calibration is a fundamental property of a good predictive model: it req...
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10/03/2022
Optimizing Data Collection for Machine Learning
Modern deep learning systems require huge data sets to achieve impressiv...
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07/04/2022
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Given a small training data set and a learning algorithm, how much more ...
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02/08/2022
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Modern computer vision applications rely on learning-based perception mo...
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04/22/2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
Linear interpolation between initial neural network parameters and conve...
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12/10/2020
Flexible Few-Shot Learning with Contextual Similarity
Existing approaches to few-shot learning deal with tasks that have persi...
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10/14/2020
Theoretical bounds on estimation error for meta-learning
Machine learning models have traditionally been developed under the assu...
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07/13/2020
Regularized linear autoencoders recover the principal components, eventually
Our understanding of learning input-output relationships with neural net...
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11/06/2019
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
Posterior collapse in Variational Autoencoders (VAEs) arises when the va...
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11/03/2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Lipschitz constraints under L2 norm on deep neural networks are useful f...
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07/19/2019
Lookahead Optimizer: k steps forward, 1 step back
The vast majority of successful deep neural networks are trained using v...
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11/13/2018
Sorting out Lipschitz function approximation
Training neural networks subject to a Lipschitz constraint is useful for...
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06/27/2018
Adversarial Distillation of Bayesian Neural Network Posteriors
Bayesian neural networks (BNNs) allow us to reason about uncertainty in ...
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04/01/2018
