9 citations · 14 across the 12 of their papers we have counts for
13 papers
Anchoring What Matters: A Dual-Level Learning Framework for Visually-Grounded Multimodal Reasoning
Xinxin Song, Siyuan Li, Tingxiong Xiao +1
Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capabilities of large vision-language models (LVLMs). However, standard on-policy RLV…
ExBind: A Controlled Diagnostic Benchmark for Visual-to-Executable Correspondence
Ziqian Wang, Yuxiao Cheng, Tingxiong Xiao +1
Multimodal coding and editing systems must map a visible or semantic referent to the exact executable object that can be edited. A wrong reference may select a valid but incorrect…
CARE: Contrastive Anchor-based Rubric Evolution for Large Language Model Post-Training
Siyuan Li, Xinxin Song, Chen Ruinian +5
Rubric-based reinforcement learning decomposes open-ended instructions into prompt-specific, flexible rubrics, making it better suited than reinforcement learning with verifiable r…
EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
Ziqian Wang, Tingxiong Xiao, Yuxiao Cheng +1
Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations. We prop…
A Unified Data-Driven Framework for Efficient Scientific Discovery
Tingxiong Xiao, Xinxin Song, Ziqian Wang +2
Scientific discovery drives progress across disciplines, from fundamental physics to industrial applications. However, identifying physical laws automatically from gathered dataset…
A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging
Ruoran Li, Runzhao Yang, Wenxin Xiang +3
Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression. However, fMRI compressing poses u…
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao +3
Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performan…
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Tingxiong Xiao, Weihang Zhang, Yuxiao Cheng +1
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring makin…
SHoP: A Deep Learning Framework for Solving High-order Partial Differential Equations
Tingxiong Xiao, Runzhao Yang, Yuxiao Cheng +2
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research. Due to…
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng, Lianglong Li, Tingxiong Xiao +4
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researcher…
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao +4
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and hig…
TINC: Tree-structured Implicit Neural Compression
Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng +2
Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique…
SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
Runzhao Yang, Tingxiong Xiao, Yuxiao Cheng +4
Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression…