Speaker Diarization with LSTM. - Quan Wang; Carlton Downey; Li Wan 004; Philip Andrew Mansfield; Ignacio López-Moreno (ICASSP, 2018)

Links: A High-Dimensional Online Clustering Method. - Philip Andrew Mansfield; Quan Wang; Carlton Downey; Li Wan 004; Ignacio López-Moreno (CoRR, 2018)

Contrastive Learning for Label Efficient Semantic Segmentation. - Xiangyun Zhao; Raviteja Vemulapalli; Philip Andrew Mansfield; Boqing Gong; Bradley Green; Lior Shapira; Ying Wu 001 (ICCV, 2021)

Camera View Adjustment Prediction for Improving Image Composition. - Yu-Chuan Su; Raviteja Vemulapalli; Ben Weiss; Chun-Te Chu; Philip Andrew Mansfield; Lior Shapira; Colvin Pitts (CoRR, 2021)

Federated Training of Dual Encoding Models on Small Non-IID Client Datasets. - Raviteja Vemulapalli; Warren Richard Morningstar; Philip Andrew Mansfield; Hubert Eichner; Karan Singhal; Arash Afkanpour; Bradley Green (CoRR, 2022)

Large Language Models Encode Clinical Knowledge. - Karan Singhal; Shekoofeh Azizi; Tao Tu 001; S. Sara Mahdavi; Jason Wei; Hyung Won Chung; Nathan Scales; Ajay Kumar Tanwani; Heather Cole-Lewis; Stephen Pfohl; Perry Payne; Martin Seneviratne; et al. (CoRR, 2022)

Random field augmentations for self-supervised representation learning. - Philip Andrew Mansfield; Arash Afkanpour; Warren Richard Morningstar; Karan Singhal (NeurReps, 2023)

Towards Expert-Level Medical Question Answering with Large Language Models. - Karan Singhal; Tao Tu 001; Juraj Gottweis; Rory Sayres; Ellery Wulczyn; Le Hou; Kevin Clark; Stephen Pfohl; Heather Cole-Lewis; Darlene Neal; Mike Schaekermann; Amy Wang; et al. (CoRR, 2023)

Towards Generalist Biomedical AI. - Tao Tu 001; Shekoofeh Azizi; Danny Driess; Mike Schaekermann; Mohamed Amin; Pi-Chuan Chang; Andrew Carroll; Chuck Lau; Ryutaro Tanno; Ira Ktena; Basil Mustafa; Aakanksha Chowdhery; et al. (CoRR, 2023)

Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout. - Pengfei Guo; Warren Richard Morningstar; Raviteja Vemulapalli; Karan Singhal; Vishal M. Patel; Philip Andrew Mansfield (CoRR, 2023)

Augmentations vs Algorithms: What Works in Self-Supervised Learning. - Warren R. Morningstar; Alex Bijamov; Chris Duvarney; Luke Friedman; Neha Mukund Kalibhat; Luyang Liu; Philip Andrew Mansfield; Renan A. Rojas-Gomez; Karan Singhal; Bradley Green; Sushant Prakash (CoRR, 2024)

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models. - Stephen R. Pfohl; Heather Cole-Lewis; Rory Sayres; Darlene Neal; Mercy Nyamewaa Asiedu; Awa Dieng; Nenad Tomasev; Qazi Mamunur Rashid; Shekoofeh Azizi; Negar Rostamzadeh; Liam G. McCoy; Leo Anthony Celi; et al. (CoRR, 2024)

Capabilities of Gemini Models in Medicine. - Khaled Saab 003; Tao Tu 001; Wei-Hung Weng; Ryutaro Tanno; David Stutz; Ellery Wulczyn; Fan Zhang; Tim Strother; Chunjong Park; Elahe Vedadi; Juanma Zambrano Chaves; Szu-Yeu Hu; et al. (CoRR, 2024)

SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer. - Renan A. Rojas-Gomez; Karan Singhal; Ali Etemad; Alex Bijamov; Warren Richard Morningstar; Philip Andrew Mansfield (Trans. Mach. Learn. Res., 2024)

Federated Variational Inference: Towards Improved Personalization and Generalization. - Elahe Vedadi; Joshua V. Dillon; Philip Andrew Mansfield; Karan Singhal; Arash Afkanpour; Warren Richard Morningstar (Trans. Mach. Learn. Res., 2024)

Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations. - Neha Mukund Kalibhat; Warren Richard Morningstar; Alex Bijamov; Luyang Liu; Karan Singhal; Philip Andrew Mansfield (UniReps, 2025)

Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning. - Yuhan Liu; Lin Ning 001; Neo Wu; Karan Singhal; Philip Andrew Mansfield; Devora Berlowitz; Sushant Prakash; Bradley Green (CoRR, 2025)