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04/21/2022
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
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06/14/2021
Hierarchically Regularized Deep Forecasting
Hierarchical forecasting is a key problem in many practical multivariate...
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04/12/2020
Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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03/15/2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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05/30/2018
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
Many real world applications can be framed as multi-objective optimizati...
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11/15/2016
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
In this work we use the recent advances in representation learning to pr...
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04/10/2016