42 citations · 96 across the 9 of their papers we have counts for
9 papers
Learning Temporal Rules from Noisy Timeseries Data
Karan Samel, Zelin Zhao, Binghong Chen +4
Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose highe…
ProTo: Program-Guided Transformer for Program-Guided Tasks
Zelin Zhao, Karan Samel, Binghong Chen +1
Programs, consisting of semantic and structural information, play an important role in the communication between humans and agents. Towards learning general program executors to un…
Graph Contrastive Pre-training for Effective Theorem Reasoning
Zhaoyu Li, Binghong Chen, Xujie Si
Interactive theorem proving is a challenging and tedious process, which requires non-trivial expertise and detailed low-level instructions (or tactics) from human experts. Tactic p…
BioNavi-NP: Biosynthesis Navigator for Natural Products
Shuangjia Zheng, Tao Zeng, Chengtao Li +4
Nature, a synthetic master, creates more than 300,000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs. To date,…
How to Design Sample and Computationally Efficient VQA Models
Karan Samel, Zelin Zhao, Binghong Chen +3
In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested. Previous models propose different methods for…
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Binghong Chen, Chengtao Li, Hanjun Dai +1
Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possib…
GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen +4
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an …
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
Binghong Chen, Bo Dai, Qinjie Lin +3
We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in hig…
A Communication-Efficient Parallel Method for Group-Lasso
Binghong Chen, Jun Zhu
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables. However, most existing…