823 citations · 1k across the 10 of their papers we have counts for
11 papers
CodeEvolve: LLM-Driven Evolutionary Optimization with Runtime-Enriched Target Selection for Multi-Language Code Enhancement
Ajay Krishna Borra, Wenzhuo Yang, Samarth Arora +9
We present CodeEvolve, an evolutionary framework for improving program performance and code quality with Large Language Models (LLMs). CodeEvolve extends OpenEvolve with runtime-gu…
PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback
Yun Peng, Akhilesh Deepak Gotmare, Michael Lyu +3
Large Language Models (LLMs) are widely adopted for assisting in software development tasks, yet their performance evaluations have narrowly focused on the functional correctness o…
CodeTF: One-stop Transformer Library for State-of-the-art Code LLMs
Nghi D. Q. Bui, Hung Le, Yue Wang +3
Code intelligence plays a key role in transforming modern software engineering. Recently, deep learning-based models, especially Transformer-based large language models (LLMs), hav…
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
Yue Wang, Hung Le, Akhilesh Deepak Gotmare +3
Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of…
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare +2
Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have sh…
Cascaded Fast and Slow Models for Efficient Semantic Code Search
Akhilesh Deepak Gotmare, Junnan Li, Shafiq Joty +1
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approa…
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare +3
Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimod…
GeDi: Generative Discriminator Guided Sequence Generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann +4
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of…
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong +1
The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip…
Using Mode Connectivity for Loss Landscape Analysis
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong +1
Mode connectivity is a recently introduced frame- work that empirically establishes the connected- ness of minima by finding a high accuracy curve between two independently trained…
Unsupervised robust nonparametric learning of hidden community properties
Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi
We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according t…