Search Results for author: Binghong Chen

Found 18 papers, 4 papers with code

PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer

no code implementations1 Sep 2023 Rui Feng, Huan Tran, Aubrey Toland, Binghong Chen, Qi Zhu, Rampi Ramprasad, Chao Zhang

Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields.

Learning to Improve Code Efficiency

no code implementations9 Aug 2022 Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

To automatically learn these hints from the dataset, we propose a novel discrete variational auto-encoder, where each discrete latent variable represents a different learned category of code-edit that increases performance.

Learning Temporal Rules from Noisy Timeseries Data

no code implementations11 Feb 2022 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

ProTo: Program-Guided Transformer for Program-Guided Tasks

1 code implementation NeurIPS 2021 Zelin Zhao, Karan Samel, Binghong Chen, Le Song

Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program.

Decision Making Learning to Execute +2

Neural Temporal Logic Programming

no code implementations29 Sep 2021 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

Spanning Tree-based Graph Generation for Molecules

no code implementations ICLR 2022 Sungsoo Ahn, Binghong Chen, Tianzhe Wang, Le Song

In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.

Graph Generation Molecular Graph Generation

Graph Contrastive Pre-training for Effective Theorem Reasoning

no code implementations24 Aug 2021 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.

Automated Theorem Proving Contrastive Learning +1

BioNavi-NP: Biosynthesis Navigator for Natural Products

no code implementations26 May 2021 Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu

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.


Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

no code implementations25 Apr 2021 Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine Tang, Lucia Mirabella, Le Song, Glaucio H. Paulino

We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems.

How to Design Sample and Computationally Efficient VQA Models

no code implementations22 Mar 2021 Karan Samel, Zelin Zhao, Binghong Chen, Kuan Wang, Robin Luo, Le Song

In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested.

Question Answering Visual Question Answering

Molecule Optimization by Explainable Evolution

no code implementations ICLR 2021 Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.

Diversity Drug Discovery

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

no code implementations1 Jan 2021 Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song

We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.

Domain Adaptation Retrosynthesis

Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA

no code implementations1 Jan 2021 Karan Samel, Zelin Zhao, Kuan Wang, Robin Luo, Binghong Chen, Le Song

We present a differentiable end-to-end program executor (DePe), which addresses Visual Question Answering (VQA) in a sample and computationally efficient manner.

Question Answering Visual Question Answering

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

1 code implementation ICML 2020 Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

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.

Multi-step retrosynthesis

GLAD: Learning Sparse Graph Recovery

1 code implementation ICLR 2020 Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song

Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.

Inductive Bias

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

1 code implementation ICLR 2020 Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

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 high dimensional continuous state and action spaces.

Vocal Bursts Intensity Prediction

A Communication-Efficient Parallel Method for Group-Lasso

no code implementations7 Dec 2016 Binghong Chen, Jun Zhu

Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables.


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