5 papers · 1 filter
What Keeps Vision-Language Models Looking at the Image?
Hiroto Osaka, Shohei Taniguchi, Gouki Minegishi +3
When do vision-language models need direct access to the image while generating an answer? We study image dependence during answer generation by examining how the visual informatio…
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Ku Onoda, Paavo Parmas, Hiroki Furuta +4
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. Th…
Residual Koopman Spectral Profiling for Predicting and Preventing Transformer Training Instability
Bum Jun Kim, Shohei Taniguchi, Makoto Kawano +2
Training divergence in transformers wastes compute, yet practitioners discover instability only after expensive runs begin. They therefore need an expected probability of failure f…
Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers
Atsushi Shimizu, Shohei Taniguchi, Yutaka Matsuo
Length generalization is the ability of language models to maintain performance on inputs longer than those seen during pretraining. In this work, we introduce a simple yet powerfu…
-MoE: Generalizing Mixture of Experts to Infinite Experts
Shota Takashiro, Takeshi Kojima, Shohei Taniguchi +2
The Mixture of Experts (MoE) selects a few feed-forward networks (FFNs) per token, achieving an effective trade-off between computational cost and performance. In conventional MoE,…