Tootfinder

DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, Ming-Yu Liu
https://arxiv.org/abs/2509.16117

DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet they inherit fundamental drawbacks, including solver restrictions, forward-reverse inconsistency, and complicated integration with classifier-free guidance (CFG). We introduce Diffusion Negative-aware FineTuning (DiffusionNFT), a new o…

Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits
Xuhao Zhu, Zuoheng Zou, Feitong Jin, Pavel Mosharev, Maolin Luo, Yaozu Wu, Jiachen Chen, Chuanyu Zhang, Yu Gao, Ning Wang, Yiren Zou, Aosai Zhang, Fanhao Shen, Zehang Bao, Zitian Zhu, Jiarun Zhong, Zhengyi Cui, Yihang Han, Yiyang He, Han Wang, Jia-Nan Yang, Yanzhe Wang, Jiayuan Shen, Gongyu Liu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Chao Song, Zhen Wang, Hekang Li, Qiujiang Gu…

Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits
A pivotal task for quantum computing is to speed up solving problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad applicability and natural fitness to Ising Hamiltonians. Here we propose a quantum sampling strategy, based on which we design an algorithm for accelerating solving the ground states of Ising model, a class of NP-hard problems in combinatorial optimization. The …

Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun
https://arxiv.org/abs/2509.23068 https:…

Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Recent advances in deep learning highlight the need for personalized models that can learn from small or moderate samples, handle high dimensional features, and remain interpretable. To address this challenge, we propose the Sparse Deep Additive Model with Interactions (SDAMI), a framework that combines sparsity driven feature selection with deep subnetworks for flexible function approximation. Unlike conventional deep learning models, which often function as black boxes, SDAMI explicitly disen…

Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild
Jing-Tong Tzeng, Bo-Hao Su, Ya-Tse Wu, Hsing-Hang Chou, Chi-Chun Lee
https://arxiv.org/abs/2508.07282

Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild
In this study, we revisit key training strategies in machine learning often overlooked in favor of deeper architectures. Specifically, we explore balancing strategies, activation functions, and fine-tuning techniques to enhance speech emotion recognition (SER) in naturalistic conditions. Our findings show that simple modifications improve generalization with minimal architectural changes. Our multi-modal fusion model, integrating these optimizations, achieves a valence CCC of 0.6953, the best v…