Biswajit Paria | DeepAI

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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...

5 Abhimanyu Das, et al. ∙

∙ 06/14/2021

Hierarchically Regularized Deep Forecasting

Hierarchical forecasting is a key problem in many practical multivariate...

11 Biswajit Paria, et al. ∙

∙ 04/12/2020

Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted a...

20 Biswajit Paria, et al. ∙

∙ 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...

14 Kirthevasan Kandasamy, et al. ∙

∙ 05/30/2018

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations

Many real world applications can be framed as multi-objective optimizati...

0 Biswajit Paria, et al. ∙

∙ 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...

0 Biswajit Paria, et al. ∙

∙ 04/10/2016

Visualization Regularizers for Neural Network based Image Recognition

The success of deep neural networks is mostly due their ability to learn...

0 Biswajit Paria, et al. ∙

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