2 citations · 3 across the 3 of their papers we have counts for
4 papers
Large Language Models of Code Fail at Completing Code with Potential Bugs
Tuan Dinh, Jinman Zhao, Samson Tan +4
Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. Howev…
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations
Longxiang Zhang, Renato Negrinho, Arindam Ghosh +4
Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long…
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization
Manuel Madeira, Renato Negrinho, João Xavier +1
First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning. Variance reduction for these algorithms has become…
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization
Lourenço V. Pato, Renato Negrinho, Pedro M. Q. Aguiar
The majority of current object detectors lack context: class predictions are made independently from other detections. We propose to incorporate context in object detection by post…