no code implementations • 9 Oct 2023 • Justin Lee, Tuomas Oikarinen, Arjun Chatha, Keng-Chi Chang, Yilan Chen, Tsui-Wei Weng
Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast.
no code implementations • 29 Aug 2023 • Mustafa Eyceoz, Justin Lee, Siddharth Pittie, Homayoon Beigi
Most state-of-the-art spoken language identification models are closed-set; in other words, they can only output a language label from the set of classes they were trained on.
no code implementations • 7 Jun 2023 • Tan H. Nguyen, Dinkar Juyal, Jin Li, Aaditya Prakash, Shima Nofallah, Chintan Shah, Sai Chowdary Gullapally, Limin Yu, Michael Griffin, Anand Sampat, John Abel, Justin Lee, Amaro Taylor-Weiner
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
no code implementations • 24 May 2023 • Mahla Abdolahnejad, Justin Lee, Hannah Chan, Alex Morzycki, Olivier Ethier, Anthea Mo, Peter X. Liu, Joshua N. Wong, Colin Hong, Rakesh Joshi
We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images.
no code implementations • 3 May 2023 • Sai Chowdary Gullapally, Yibo Zhang, Nitin Kumar Mittal, Deeksha Kartik, Sandhya Srinivasan, Kevin Rose, Daniel Shenker, Dinkar Juyal, Harshith Padigela, Raymond Biju, Victor Minden, Chirag Maheshwari, Marc Thibault, Zvi Goldstein, Luke Novak, Nidhi Chandra, Justin Lee, Aaditya Prakash, Chintan Shah, John Abel, Darren Fahy, Amaro Taylor-Weiner, Anand Sampat
Machine learning algorithms have the potential to improve patient outcomes in digital pathology.
no code implementations • 20 May 2022 • Mustafa Eyceoz, Justin Lee, Homayoon Beigi
While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem.
1 code implementation • Findings (ACL) 2022 • Justin Lee, Sowmya Vajjala
Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research.
1 code implementation • 4 Jul 2019 • Derek Howard, Marta Maslej, Justin Lee, Jacob Ritchie, Geoffrey Woollard, Leon French
We used TPOT and auto-sklearn as AutoML tools to generate classifiers to triage the posts.
no code implementations • 22 Feb 2017 • Ayan Sinha, Justin Lee, Shuai Li, George Barbastathis
Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks.