Zelda Mariet | DeepAI

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∙ 06/06/2023

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions

Antibodies have become an important class of therapeutic agents to treat...

0 Hirofumi Tsuruta, et al. ∙

∙ 07/07/2022

Pre-training helps Bayesian optimization too

Bayesian optimization (BO) has become a popular strategy for global opti...

17 Zi Wang, et al. ∙

∙ 06/21/2022

Ensembling over Classifiers: a Bias-Variance Perspective

Ensembles are a straightforward, remarkably effective method for improvi...

0 Neha Gupta, et al. ∙

∙ 02/08/2022

Understanding the bias-variance tradeoff of Bregman divergences

This paper builds upon the work of Pfau (2013), which generalized the bi...

0 Ben Adlam, et al. ∙

∙ 09/16/2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers

The performance of deep neural networks can be highly sensitive to the c...

9 Zi Wang, et al. ∙

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

∙ 06/05/2020

Population-Based Black-Box Optimization for Biological Sequence Design

The use of black-box optimization for the design of new biological seque...

1 Christof Angermueller, et al. ∙

∙ 02/23/2020

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling

Many contemporary machine learning models require extensive tuning of hy...

0 Setareh Ariafar, et al. ∙

∙ 01/07/2019

DPPNet: Approximating Determinantal Point Processes with Deep Networks

Determinantal Point Processes (DPPs) provide an elegant and versatile wa...

0 Zelda Mariet, et al. ∙

∙ 05/09/2018

Foundations of Sequence-to-Sequence Modeling for Time Series

The availability of large amounts of time series data, paired with the p...

0 Vitaly Kuznetsov, et al. ∙

∙ 02/15/2018

Learning Determinantal Point Processes by Sampling Inferred Negatives

Determinantal Point Processes (DPPs) have attracted significant interest...

0 Zelda Mariet, et al. ∙

∙ 05/26/2016

Kronecker Determinantal Point Processes

Determinantal Point Processes (DPPs) are probabilistic models over all s...

0 Zelda Mariet, et al. ∙

∙ 11/16/2015

Diversity Networks: Neural Network Compression Using Determinantal Point Processes

We introduce Divnet, a flexible technique for learning networks with div...

0 Zelda Mariet, et al. ∙

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