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06/21/2023
Constant Memory Attention Block
Modern foundation model architectures rely on attention mechanisms to ef...
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05/23/2023
Constant Memory Attentive Neural Processes
Neural Processes (NPs) are efficient methods for estimating predictive u...
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01/27/2023
Meta Temporal Point Processes
A temporal point process (TPP) is a stochastic process where its realiza...
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11/19/2022
Gumbel-Softmax Selective Networks
ML models often operate within the context of a larger system that can a...
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11/15/2022
Latent Bottlenecked Attentive Neural Processes
Neural Processes (NPs) are popular methods in meta-learning that can est...
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06/17/2022
Stop Overcomplicating Selective Classification: Use Max-Logit
We tackle the problem of Selective Classification where the goal is to a...
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05/17/2022
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification
We study settings where gradient penalties are used alongside risk minim...
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10/18/2019
Point Process Flows
Event sequences can be modeled by temporal point processes (TPPs) to cap...
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04/07/2019
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
Objective: The aim of this study is to develop an efficient and reliable...
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10/10/2018
Combining Bayesian Optimization and Lipschitz Optimization
Bayesian optimization and Lipschitz optimization have developed alternat...
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11/05/2015
Stop Wasting My Gradients: Practical SVRG
We present and analyze several strategies for improving the performance ...
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04/16/2015
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
We apply stochastic average gradient (SAG) algorithms for training condi...
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03/12/2012