66 citations · 97 across the 8 of their papers we have counts for
8 papers
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…
Sampling weights of deep neural networks
Erik Lien Bolager, Iryna Burak, Chinmay Datar +2
We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a supervised learning context,…
Exploring Example Influence in Continual Learning
Qing Sun, Fan Lyu, Fanhua Shang +2
Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new ta…
Learning to Revise References for Faithful Summarization
Griffin Adams, Han-Chin Shing, Qing Sun +3
In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news cor…
Neural Entity Recognition with Gazetteer based Fusion
Qing Sun, Parminder Bhatia
Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only li…
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
Kristjan Arumae, Qing Sun, Parminder Bhatia
Pre-training large language models has become a standard in the natural language processing community. Such models are pre-trained on generic data (e.g. BookCorpus and English Wiki…