Satwik Bhattamishra | DeepAI

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∙ 07/31/2023

Structural Transfer Learning in NL-to-Bash Semantic Parsers

Large-scale pre-training has made progress in many fields of natural lan...

0 Kyle Duffy, et al. ∙

∙ 06/20/2023

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization

With the increase in the scale of Deep Learning (DL) training workloads ...

0 Amey Agrawal, et al. ∙

∙ 11/22/2022

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions

Despite the widespread success of Transformers on NLP tasks, recent work...

0 Satwik Bhattamishra, et al. ∙

∙ 03/14/2022

Revisiting the Compositional Generalization Abilities of Neural Sequence Models

Compositional generalization is a fundamental trait in humans, allowing ...

0 Arkil Patel, et al. ∙

∙ 03/12/2021

Are NLP Models really able to Solve Simple Math Word Problems?

The problem of designing NLP solvers for math word problems (MWP) has se...

0 Arkil Patel, et al. ∙

∙ 11/08/2020

On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

While recurrent models have been effective in NLP tasks, their performan...

0 Satwik Bhattamishra, et al. ∙

∙ 09/23/2020

On the Ability of Self-Attention Networks to Recognize Counter Languages

Transformers have supplanted recurrent models in a large number of NLP t...

0 Satwik Bhattamishra, et al. ∙

∙ 06/16/2020

On the Computational Power of Transformers and Its Implications in Sequence Modeling

Transformers are being used extensively across several sequence modeling...

0 Satwik Bhattamishra, et al. ∙

∙ 12/07/2019

Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities

In this paper, we examine and analyze the challenges associated with dev...

0 Pratik Joshi, et al. ∙

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