P
Philip Andrew Mansfield
Researcher at Google
Publications - 41
Citations - 1396
Philip Andrew Mansfield is an academic researcher from Google. The author has contributed to research in topics: Structured document & Set (abstract data type). The author has an hindex of 16, co-authored 37 publications receiving 1055 citations. Previous affiliations of Philip Andrew Mansfield include Apple Inc. & Mansfield University of Pennsylvania.
Papers
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Proceedings ArticleDOI
Speaker Diarization with LSTM
TL;DR: In this paper, the authors combine LSTM-based d-vector audio embeddings with recent work in nonparametric clustering to obtain a state-of-the-art speaker diarization system.
Patent
Method, system, and graphical user interface for text entry with partial word display
TL;DR: A computer-implemented method for text entry includes receiving entered text from a user, selecting a set of candidate sequences for completing or continuing the sequence, and presenting the candidate sequences to the user, wherein the candidate sequence include partial words.
Posted Content
Speaker Diarization with LSTM
TL;DR: This work combines LSTM-based d-vector audio embeddings with recent work in nonparametric clustering to obtain a state-of-the-art speaker diarization system that achieves a 12.0% diarization error rate on NIST SRE 2000 CALLHOME, while the model is trained with out- of-domain data from voice search logs.
Journal ArticleDOI
Large Language Models Encode Clinical Knowledge
Karan Singhal,Shekoofeh Azizi,Tao Tu,S Mahdavi,Jason Loh Seong Wei,Hyung Won Chung,Nathan Scales,Ajay Kumar Tanwani,Heather Cole-Lewis,Stephen Pfohl,P. A. Payne,Martin G. Seneviratne,P. Gamble,Chris Kelly,Nathaneal Scharli,Aakanksha Chowdhery,Philip Andrew Mansfield,Blaise Aguera y Arcas,Dale R. Webster,Greg S. Corrado,Yossi Matias,K. Chou,Juraj Gottweis,Nenad Tomasev,Yun Liu,Alvin Rajkomar,Joëlle K. Barral,Christopher Semturs,Alan Karthikesalingam,Vivek T. Natarajan +29 more
TL;DR: The authors proposed a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias, and showed that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.
Posted Content
Contrastive Learning for Label-Efficient Semantic Segmentation
Xiangyun Zhao,Raviteja Vemulapalli,Philip Andrew Mansfield,Boqing Gong,Bradley Ray Green,Lior Shapira,Ying Wu +6 more
TL;DR: A simple and effective contrastive learning-based training strategy in which the network is pretrain the network using a pixel-wise, label-based contrastive loss, and then fine-tune it using the cross-entropy loss, which increases intra-class compactness and inter-class separability, thereby resulting in a better pixel classifier.