5 citations · 21 across the 8 of their papers we have counts for
8 papers
What's Left? Concept Grounding with Logic-Enhanced Foundation Models
Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum +1
Recent works such as VisProg and ViperGPT have smartly composed foundation models for visual reasoning-using large language models (LLMs) to produce programs that can be executed b…
Motion Question Answering via Modular Motion Programs
Mark Endo, Joy Hsu, Jiaman Li +1
In order to build artificial intelligence systems that can perceive and reason with human behavior in the real world, we must first design models that conduct complex spatio-tempor…
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Renhao Wang, Jiayuan Mao, Joy Hsu +3
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent w…
NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations
Joy Hsu, Jiayuan Mao, Jiajun Wu
Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipul…
Geoclidean: Few-Shot Generalization in Euclidean Geometry
Joy Hsu, Jiajun Wu, Noah D. Goodman
Euclidean geometry is among the earliest forms of mathematical thinking. While the geometric primitives underlying its constructions, such as perfect lines and circles, do not ofte…
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
Joy Hsu, Wah Chiu, Serena Yeung
In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled…
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Joy Hsu, Jeffrey Gu, Gong-Her Wu +2
We consider the task of representation learning for unsupervised segmentation of 3D voxel-grid biomedical images. We show that models that capture implicit hierarchical relationshi…
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
Joy Hsu, Sonia Phene, Akinori Mitani +4
As machine learning has become increasingly applied to medical imaging data, noise in training labels has emerged as an important challenge. Variability in diagnosis of medical ima…