3 papers
cs.CV2026
: A "Spot the Difference" Challenge for Large Multimodal Models
Kewei Wei, Bocheng Hu, Jie Cao +13
Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend…
cs.LG2026
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi +6
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning l…
cs.SE2025
A Comprehensive Study on the Use of Word Embedding Models in Software Engineering Domain
Xiaohan Chen, Weiqin Zou, Lianyi Zhi +2
Word embedding (WE) techniques are advanced textual semantic representation models oriented from the natural language processing (NLP) area. Inspired by their effectiveness in faci…