1 citations · 1 across the 8 of their papers we have counts for
9 papers
SDFlow: Similarity-Driven Flow Matching for Time Series Generation
Wei Li, Shibo Feng, Pengcheng Wu +3
Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. However, such models are fundamenta…
PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction
Ziqiao Meng, Qichao Wang, Zhiyang Dou +4
Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an art…
Multi-Task Vehicle Routing Solver via Mixture of Specialized Experts under State-Decomposable MDP
Yuxin Pan, Zhiguang Cao, Chengyang Gu +4
Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutili…
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction
Qichao Wang, Ziqiao Meng, Wenqian Cui +6
Inspired by the impressive capabilities of GPT-4o, there is growing interest in enabling speech language models (SLMs) to engage in natural, fluid spoken interactions with humans.…
Injecting Imbalance Sensitivity for Multi-Task Learning
Zhipeng Zhou, Liu Liu, Peilin Zhao +1
Multi-task learning (MTL) has emerged as a promising approach for deploying deep learning models in real-life applications. Recent studies have proposed optimization-based learning…
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…
PointNSP: Autoregressive 3D Point Cloud Generation with Next-Scale Level-of-Detail Prediction
Ziqiao Meng, Qichao Wang, Zhiyang Dou +4
Autoregressive point cloud generation has long lagged behind diffusion-based approaches in quality. The performance gap stems from the fact that autoregressive models impose an art…
HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
Shibo Feng, Peilin Zhao, Liu Liu +2
Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting…
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Chengqian Gao, Haonan Li, Liu Liu +3
The alignment of large language models (LLMs) often assumes that using more clean data yields better outcomes, overlooking the match between model capacity and example difficulty.…