activity
20202024
most citedBelief Propagation Neural Networks

16 citations · 30 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SE2024★ 3 cited

Code Repair with LLMs gives an Exploration-Exploitation Tradeoff

Hao Tang, Keya Hu, Jin Peng Zhou +4

Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too comple…

cs.AI2024★ 3 cited

WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment

Hao Tang, Darren Key, Kevin Ellis

We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment. The world model tries to explain it…

cs.AI2022

From Perception to Programs: Regularize, Overparameterize, and Amortize

Hao Tang, Kevin Ellis

Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into…

cs.LG2021

Equivariant Neural Network for Factor Graphs

Fan-Yun Sun, Jonathan Kuck, Hao Tang +1

Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution. An algorithm that performs inference on a factor gra…

cs.CV2020★ 5 cited

Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals

Tongzhou Mu, Jiayuan Gu, Zhiwei Jia +2

We study how to learn a policy with compositional generalizability. We propose a two-stage framework, which refactorizes a high-reward teacher policy into a generalizable student p…

cs.LG2020★ 3 cited

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

Hao Tang, Zhiao Huang, Jiayuan Gu +2

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Tak…

cs.LG2020★ 16 cited

Belief Propagation Neural Networks

Jonathan Kuck, Shuvam Chakraborty, Hao Tang +4

Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still l…