245 citations · 783 across the 82 of their papers we have counts for
6 papers · 1 filter
Perspectives on the State and Future of Deep Learning - 2023
Micah Goldblum, Anima Anandkumar, Richard Baraniuk +7
The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey perio…
Deep Multimodal Fusion for Surgical Feedback Classification
Rafal Kocielnik, Elyssa Y. Wong, Timothy N. Chu +5
Quantification of real-time informal feedback delivered by an experienced surgeon to a trainee during surgery is important for skill improvements in surgical training. Such feedbac…
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Adhithya Prakash Saravanan, Rafal Kocielnik, Roy Jiang +2
Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing. Characterising the implicit social biases they exhibit, su…
Plasma Surrogate Modelling using Fourier Neural Operators
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +10
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rap…
EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
Benyamin Haghi, Lin Ma, Sahin Lale +2
We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and…
Eureka: Human-Level Reward Design via Coding Large Language Models
Yecheng Jason Ma, William Liang, Guanzhi Wang +6
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation ta…