1 citations · 1 across the 2 of their papers we have counts for
5 papers
Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models
Zhifei Xie, Ziyang Ma, Zihang Liu +7
Reasoning is essential for effective communication and decision-making. While recent advances in LLMs and MLLMs have shown that incorporating explicit reasoning significantly impro…
Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Shuaicheng Niu, Guohao Chen, Deyu Chen +7
Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, 3) online imbalanced labe…
Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
Wanjin Feng, Xingyu Gao, Wenqian Du +4
Spiking Neural Networks (SNNs) often suffer from high time complexity due to the sequential processing of spikes, making training computationally expensive. In this pape…
Continual Optimization with Symmetry Teleportation for Multi-Task Learning
Zhipeng Zhou, Ziqiao Meng, Pengcheng Wu +2
Multi-task learning (MTL) is a widely explored paradigm that enables the simultaneous learning of multiple tasks using a single model. Despite numerous solutions, the key issues of…
Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models
Zhifei Xie, Mingbao Lin, Zihang Liu +3
Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio…