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Found 7 papers, 5 papers with code
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems
There is a pressing need for a unifying theory to illuminate the commonalities and differences in approaches and enable further progress.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems.
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph.
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Backed by our theoretical analysis, instead of maximizing the recovery of cross-instance node dependencies -- which has been considered the key behind closing the performance gap between model aggregation and centralized training -- , our framework leverages randomized assignment of nodes or super-nodes (i. e., collections of original nodes) to partition the training graph such that it improves data uniformity and minimizes the discrepancy of gradient and loss function across instances.
Emotion Recognition in Conversation using Probabilistic Soft Logic
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
NeuPSL: Neural Probabilistic Soft Logic
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.
HyperFair: A Soft Approach to Integrating Fairness Criteria
In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system.
