4 papers
Rethinking Test Time Scaling for Flow-Matching Generative Models
Qingtao Yu, Changlin Song, Minghao Sun +6
The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While…
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Jiaru Zou, Soumya Roy, Vinay Kumar Verma +6
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of…
NOVO: Unlearning-Compliant Vision Transformers
Soumya Roy, Soumya Banerjee, Vinay Verma +3
Machine unlearning (MUL) refers to the problem of making a pre-trained model selectively forget some training instances or class(es) while retaining performance on the remaining da…
MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering
Vinay Kumar Verma, Shreyas Sunil Kulkarni, Happy Mittal +1
Question Answering (QA) and Visual Question Answering (VQA) are well-studied problems in the language and vision domain. One challenging scenario involves multiple sources of infor…