3 papers
cs.CV2026
Adaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models
Paribesh Regmi, Qingshuang Chen, Chi Zhang +3
Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deploy…
q-bio.QM2026
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
Kishan KC, Rui Li, Paribesh Regmi +1
Heterogeneous molecular entities and their interactions, commonly depicted as a network, are crucial for advancing our systems-level understanding of biology. With recent advanceme…
cs.LG2026
Bayesian Neighborhood Adaptation for Graph Neural Networks
Paribesh Regmi, Rui Li, Kishan KC
The neighborhood scope (i.e., number of hops) where graph neural networks (GNNs) aggregate information to characterize a node's statistical property is critical to GNNs' performanc…