Search Results for author: Xujie Si

Found 14 papers, 5 papers with code

G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks

1 code implementation29 Sep 2023 Zhaoyu Li, Jinpei Guo, Xujie Si

Graph neural networks (GNNs) have recently emerged as a promising approach for solving the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional backtracking or local search SAT solvers.

Benchmarking

Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning

2 code implementations2 May 2023 Ziyan Luo, Xujie Si

Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks.

Can ChatGPT Pass An Introductory Level Functional Language Programming Course?

no code implementations29 Apr 2023 Chuqin Geng, Yihan Zhang, Brigitte Pientka, Xujie Si

The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming.

Text Summarization

Scalar Invariant Networks with Zero Bias

no code implementations15 Nov 2022 Chuqin Geng, Xiaojie Xu, Haolin Ye, Xujie Si

However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles.

Fairness Image Classification

NSNet: A General Neural Probabilistic Framework for Satisfiability Problems

1 code implementation7 Nov 2022 Zhaoyu Li, Xujie Si

We present the Neural Satisfiability Network (NSNet), a general neural framework that models satisfiability problems as probabilistic inference and meanwhile exhibits proper explainability.

Towards Reliable Neural Specifications

no code implementations28 Oct 2022 Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si

We show that by using NAP, we can verify a significant region of the input space, while still recalling 84% of the data on MNIST.

Adversarial Robustness

Novice Type Error Diagnosis with Natural Language Models

no code implementations7 Oct 2022 Chuqin Geng, Haolin Ye, Yixuan Li, Tianyu Han, Brigitte Pientka, Xujie Si

Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations.

Language Modelling Vocal Bursts Type Prediction

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

Techniques for Symbol Grounding with SATNet

1 code implementation NeurIPS 2021 Sever Topan, David Rolnick, Xujie Si

Many experts argue that the future of artificial intelligence is limited by the field's ability to integrate symbolic logical reasoning into deep learning architectures.

Logical Reasoning Visual Reasoning

Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving

no code implementations4 Dec 2019 Xujie Si, Yujia Li, Vinod Nair, Felix Gimeno

We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.

Synthesizing Datalog Programs Using Numerical Relaxation

no code implementations1 Jun 2019 Xujie Si, Mukund Raghothaman, Kihong Heo, Mayur Naik

The problem of learning logical rules from examples arises in diverse fields, including program synthesis, logic programming, and machine learning.

Program Synthesis

Learning a Meta-Solver for Syntax-Guided Program Synthesis

no code implementations ICLR 2019 Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song

Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the specification and grammar embedding and adaptive policy.

Meta-Learning Program Synthesis

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