activity
20172026
most citedCheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

1.3k citations · 1.9k across the 9 of their papers we have counts for

collaborators

9 papers

cs.RO2026

Contrastive Conceptor Activation Steering (COAST): Unlocking Vision-Language-Action Models through Hidden States

Miranda Muqing Miao, Subin Kim, Brandon Yang +1

Vision-Language-Action (VLA) models leverage powerful perceptual priors from web-scale Vision-Language Model (VLM) pre-training, yet they remain surprisingly brittle in practice, f…

cs.CL2023★ 8 cited

Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes

Simran Arora, Brandon Yang, Sabri Eyuboglu +4

A long standing goal of the data management community is to develop general, automated systems that ingest semi-structured documents and output queryable tables without human effor…

cs.CV2020★ 6 cited

Streaming Object Detection for 3-D Point Clouds

Wei Han, Zhengdong Zhang, Benjamin Caine +7

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a pr…

cs.CV2019★ 101 cited

StarNet: Targeted Computation for Object Detection in Point Clouds

Jiquan Ngiam, Benjamin Caine, Wei Han +10

Detecting objects from LiDAR point clouds is an important component of self-driving car technology as LiDAR provides high resolution spatial information. Previous work on point-clo…

cs.CV2019★ 28 cited

Using Videos to Evaluate Image Model Robustness

Keren Gu, Brandon Yang, Jiquan Ngiam +2

Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the m…

cs.CV2019★ 241 cited

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Brandon Yang, Gabriel Bender, Quoc V. Le +1

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples…