1 code implementation • ICML 2020 • Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik
The policy neural network employs a program interpreter that provides immediate feedback on the consequences of the decisions made by the policy, and also takes into account the uncertainty in the symbolic representation of the image.
no code implementations • 23 Apr 2024 • Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.
1 code implementation • 5 May 2023 • HANLIN ZHANG, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing
We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning.
no code implementations • 15 Apr 2023 • Jiani Huang, Ziyang Li, Mayur Naik, Ser-Nam Lim
We propose LASER, a neuro-symbolic approach to learn semantic video representations that capture rich spatial and temporal properties in video data by leveraging high-level logic specifications.
no code implementations • 10 Apr 2023 • Ziyang Li, Jiani Huang, Mayur Naik
We present Scallop, a language which combines the benefits of deep learning and logical reasoning.
no code implementations • NeurIPS 2021 • Jiani Huang, Ziyang Li, Binghong Chen, Karan Samel, Mayur Naik, Le Song, Xujie Si
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
no code implementations • NeurIPS Workshop DBAI 2021 • Jiani Huang, Ziyang Li, Ilias Fountalis, Mayur Naik
Numerical reasoning over text requires deep integration between the semantic understanding of the natural language context and the mathematical calculation of the symbolic terms.