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20152023
most citedA Structured Self-attentive Sentence Embedding

1.5k citations · 3.5k across the 68 of their papers we have counts for

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7 papers · 1 filter

cs.LG2023★ 1 cited

Exploring Continual Learning for Code Generation Models

Prateek Yadav, Qing Sun, Hantian Ding +9

Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance. However, libraries are upgraded or deprecated very frequently and re-training larg…

cs.LG2023★ 1 cited

Greener yet Powerful: Taming Large Code Generation Models with Quantization

Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13

ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…

cs.LG2022★ 7 cited

ReCode: Robustness Evaluation of Code Generation Models

Shiqi Wang, Zheng Li, Haifeng Qian +11

Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness…

cs.LG2022★ 29 cited

Multi-lingual Evaluation of Code Generation Models

Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang +22

We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated…

cs.LG2021★ 8 cited

Supporting Clustering with Contrastive Learning

Dejiao Zhang, Feng Nan, Xiaokai Wei +6

Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space. However, different categories often ove…

cs.LG2021★ 36 cited

Structured Prediction as Translation between Augmented Natural Languages

Giovanni Paolini, Ben Athiwaratkun, Jason Krone +6

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extractio…

cs.LG2015★ 9 cited

Distributed Deep Learning for Question Answering

Minwei Feng, Bing Xiang, Bowen Zhou

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, AD…