3 citations · 4 across the 8 of their papers we have counts for
13 papers
ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting
Shibo Feng, Wanjin Feng, Yang Qiu +3
Generative modeling has shown strong promise for multivariate time mseries (MTS) forecasting, especially scale to high-dimensional settings. Diffusion-based methods achieve competi…
Distribution-Conditioned Task Routing for Class-Incremental Learning
Longhuan Xu, Zhipeng Zhou, Wei Ji +3
Parameter-efficient adaptation enables continual learners to acquire task-specific knowledge through compact model updates while maintaining strong within-task performance. However…
MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis
Yanhao Huang, Shibo Feng, Wanjin Feng +2
Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction models. However, existing methods mainly focus on m…
VoiceMem: Streaming Dual-Brain Memory for Real-Time Interaction
Zhifei Xie, Jiaqi Lang, Ze An +7
Conversational systems, such as duplex speech language models (SLMs), still lack a streaming, accurate, and empathetic memory system as their soul. We introduce VoiceMem, a simple…
PACT: Preserving Anchored Cores in Task-vectors for Model Merging
Ningyuan Shi, Zhipeng Zhou, Hao Wang +2
Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model. Most exi…
Delve into the Applicability of Advanced Optimizers for Multi-Task Learning
Zhipeng Zhou, Linxiao Cao, Pengcheng Wu +2
Multi-Task Learning (MTL) is a foundational machine learning problem that has seen extensive development over the past decade. Recently, various optimization-based MTL approaches h…
PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
Zhifei Xie, Zongzheng Hu, Fangda Ye +10
Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambigui…
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…
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen +2
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…