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Found 7 papers, 2 papers with code
Generating Programmatic Referring Expressions via Program Synthesis
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.
Graph Machine Learning in the Era of Large Language Models (LLMs)
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.
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming
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.
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
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.
Scallop: A Language for Neurosymbolic Programming
We present Scallop, a language which combines the benefits of deep learning and logical reasoning.
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
Numerical Reasoning over Legal Contracts via Relational Database
Numerical reasoning over text requires deep integration between the semantic understanding of the natural language context and the mathematical calculation of the symbolic terms.
