14 citations · 40 across the 10 of their papers we have counts for
11 papers
RepoFusion: Training Code Models to Understand Your Repository
Disha Shrivastava, Denis Kocetkov, Harm de Vries +2
Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g.,…
Approach Intelligent Writing Assistants Usability with Seven Stages of Action
Avinash Bhat, Disha Shrivastava, Jin L. C. Guo
Despite the potential of Large Language Models (LLMs) as writing assistants, they are plagued by issues like coherence and fluency of the model output, trustworthiness, ownership o…
Repository-Level Prompt Generation for Large Language Models of Code
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge…
Minimax and Neyman-Pearson Meta-Learning for Outlier Languages
Edoardo Maria Ponti, Rahul Aralikatte, Disha Shrivastava +2
Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion. Nevertheless, the properties of these…
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a…
On-the-Fly Adaptation of Source Code Models using Meta-Learning
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation…
Transfer Learning by Modeling a Distribution over Policies
Disha Shrivastava, Eeshan Gunesh Dhekane, Riashat Islam
Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution…
Hypernyms Through Intra-Article Organization in Wikipedia
Disha Shrivastava, Sreyash Kenkre, Santosh Penubothula
We introduce a new measure for unsupervised hypernym detection and directionality. The motivation is to keep the measure computationally light and portatable across languages. We s…
Modeling Topical Coherence in Discourse without Supervision
Disha Shrivastava, Abhijit Mishra, Karthik Sankaranarayanan
Coherence of text is an important attribute to be measured for both manually and automatically generated discourse; but well-defined quantitative metrics for it are still elusive.…
A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark
Disha Shrivastava, Santanu Chaudhury, Dr. Jayadeva
Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters. In this paper, we provide a framework for…
A Machine Learning Approach for Evaluating Creative Artifacts
Disha Shrivastava, Saneem Ahmed CG, Anirban Laha +1
Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic…