23 citations · 26 across the 10 of their papers we have counts for
5 papers · 1 filter
When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models
Berkcan Kapusuzoglu, Connor Pryor, Sangwoo Cho +4
Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing low-importance experts identified by the router, assuming router probabilities pro…
A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems
Charles Dickens, Connor Pryor, Changyu Gao +5
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, a unif…
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
Charles Dickens, Changyu Gao, Connor Pryor +2
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our frame…
Emotion Recognition in Conversation using Probabilistic Soft Logic
Eriq Augustine, Pegah Jandaghi, Alon Albalak +4
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 NL…
NeuPSL: Neural Probabilistic Soft Logic
Connor Pryor, Charles Dickens, Eriq Augustine +3
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 per…