Search Results for author: Biswajit Paria

Found 12 papers, 4 papers with code

Gradient-Based Constrained Sampling from Language Models

no code implementations25 May 2022 Sachin Kumar, Biswajit Paria, Yulia Tsvetkov

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from.

Language Modelling Text Generation

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting

no code implementations21 Apr 2022 Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen

Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy.

STS Time Series +1

An Experimental Design Perspective on Model-Based Reinforcement Learning

1 code implementation9 Dec 2021 Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.

Continuous Control Experimental Design +3

An Experimental Design Perspective on Exploration in Reinforcement Learning

no code implementations ICLR 2022 Viraj Mehta, Biswajit Paria, Jeff Schneider, Willie Neiswanger, Stefano Ermon

In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.

Continuous Control Experimental Design +2

Hierarchically Regularized Deep Forecasting

no code implementations14 Jun 2021 Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy.

Time Series Time Series Analysis

Minimizing FLOPs to Learn Efficient Sparse Representations

1 code implementation ICLR 2020 Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification.

Quantization Representation Learning +1

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

1 code implementation15 Mar 2019 Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.

Bayesian Optimisation

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

no code implementations30 May 2018 Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret.

Bayesian Optimization

A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference

no code implementations15 Nov 2016 Biswajit Paria, K. M. Annervaz, Ambedkar Dukkipati, Ankush Chatterjee, Sanjay Podder

In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference.

Natural Language Inference Representation Learning

Visualization Regularizers for Neural Network based Image Recognition

2 code implementations10 Apr 2016 Biswajit Paria, Vikas Reddy, Anirban Santara, Pabitra Mitra

The success of deep neural networks is mostly due their ability to learn meaningful features from the data.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.