Search Results for author:
Found 18 papers, 4 papers with code
PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer
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
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
Events across a timeline are a common data representation, seen in different temporal modalities.
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
ProTo: Program-Guided Transformer for Program-Guided Tasks
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.
Ranked #1 on
Visual Question Answering (VQA)
on GQA test-std
Neural Temporal Logic Programming
Events across a timeline are a common data representation, seen in different temporal modalities.
Spanning Tree-based Graph Generation for Molecules
In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.
Graph Contrastive Pre-training for Effective Theorem Reasoning
Interactive theorem proving is a challenging and tedious process, which requires non-trivial expertise and detailed low-level instructions (or tactics) from human experts.
BioNavi-NP: Biosynthesis Navigator for Natural Products
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
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
In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested.
Molecule Optimization by Explainable Evolution
Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation
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.
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA
We present a differentiable end-to-end program executor (DePe), which addresses Visual Question Answering (VQA) in a sample and computationally efficient manner.
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
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.
Ranked #5 on
Multi-step retrosynthesis
on USPTO-190
GLAD: Learning Sparse Graph Recovery
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.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
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.
A Communication-Efficient Parallel Method for Group-Lasso
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables.
