activity
20222026
most citedAttention for Causal Relationship Discovery from Biological Neural Dynamics

3 citations · 3 across the 13 of their papers we have counts for

collaborators

14 papers

cs.CV2026

AstraSR: Real-World Thermal Super-Resolution with GPT-6 Astra

Mengyuan Li, Changhong Fu, Jun Zhang +3

Real-world thermal super-resolution (SR) is constrained by limited sensor resolution and the difficulty of obtaining corresponding high-resolution (HR) observations for direct mode…

cs.CV2026

RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias

Yihe Sun, Ziyu Lu, Kaihua Tang +1

Reconstructing editable Computer-Aided Design (CAD) models from images is essential for downstream modification, manufacturing, and design reuse. However, existing image-to-CAD met…

cs.AI2026

Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution

Xun Li, Yiying Yang, Pengtao Li +8

Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature. Existing methods…

cs.CV2026

FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis

Zhengyang Zhang, Ziyu Lu, PengCheng Li +6

4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However,…

cs.CV2026

CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

Hairui Zhu, Yiying Yang, Tengjin Weng +5

Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, localizing objects, segmenting regi…

cs.RO2026

OGScene3D: Incremental Open-Vocabulary 3D Gaussian Scene Graph Mapping for Scene Understanding

Siting Zhu, Ziyun Lu, Guangming Wang +5

Open-vocabulary scene understanding is crucial for robotic applications, enabling robots to comprehend complex 3D environmental contexts and supporting various downstream tasks suc…

cs.AI2026

BEAP-Agent: Backtrackable Execution and Adaptive Planning for GUI Agents

Ziyu Lu, Tengjin Weng, Yiying Yang +3

GUI agents are designed to automate repetitive tasks and enhance productivity. However, existing GUI agents struggle to recover once they follow an incorrect exploration path, ofte…

cs.CV2025

GEN3D: Generating Domain-Free 3D Scenes from a Single Image

Yuxin Zhang, Ziyu Lu, Hongbo Duan +5

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vit…

cs.LG2025

Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity

Ziyu Lu, Anna J. Li, Alexander E. Ladd +5

Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time…

cs.CL2025

MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy

Shaoxiong Zhan, Yanlin Lai, Ziyu Lu +3

Large language models have achieved substantial progress in mathematical reasoning, yet their advancement is limited by the scarcity of high-quality, high-difficulty training data.…

cs.LG2025

ProDS: Preference-oriented Data Selection for Instruction Tuning

Wenya Guo, Zhengkun Zhang, Xumeng Liu +5

Instruction data selection aims to identify a high-quality subset from the training set that matches or exceeds the performance of the full dataset on target tasks. Existing method…

cs.CV2025

AnyTSR: Any-Scale Thermal Super-Resolution for UAV

Mengyuan Li, Changhong Fu, Ziyu Lu +3

Thermal imaging can greatly enhance the application of intelligent unmanned aerial vehicles (UAV) in challenging environments. However, the inherent low resolution of thermal senso…

cs.LG2023★ 3 cited

Attention for Causal Relationship Discovery from Biological Neural Dynamics

Ziyu Lu, Anika Tabassum, Shruti Kulkarni +4

This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biop…

math.PR2022

A piecewise deterministic Markov process approach modeling a dry friction problem with noise

Josselin Garnier, Ziyu Lu, Laurent Mertz

Understanding and predicting the dynamical properties of systems involving dry friction is a major concern in physics and engineering. It abounds in many mechanical processes, from…