67 citations · 86 across the 15 of their papers we have counts for
20 papers
Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics
Tom Olesch, Nan Xiao, Xiongye Xiao +2
Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study this problem using a scene-spe…
Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction
Shaoxiang Qin, Yucheng Zhao, Zongyi Li +2
Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, sinc…
GeoWind2Plan: Mission-Time 3D Urban Wind Prediction for Energy-Efficient UAV Planning
Shaoxiang Qin, Yucheng Zhao, Fuyuan Lyu +6
In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local wind exposure as well as pa…
HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling
Peiyu Zhang, Heng Ping, Nikos Kanakaris +5
Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-oc…
HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL
Heng Ping, Shixuan Li, Peiyu Zhang +8
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, when applied to hardware description languages (HDL), t…
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance
Nikos Kanakaris, Heng Ping, Xiongye Xiao +4
Detecting organized political campaigns is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized…
ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
Shixuan Li, Wei Yang, Peiyu Zhang +6
Weather forecasting is crucial for public safety, disaster prevention and mitigation, agricultural production, and energy management, with global relevance. Although deep learning…
End-to-End Learning Framework for Solving Non-Markovian Optimal Control
Xiaole Zhang, Peiyu Zhang, Xiongye Xiao +4
Integer-order calculus often falls short in capturing the long-range dependencies and memory effects found in many real-world processes. Fractional calculus addresses these gaps vi…
Multi-scale Generative Modeling for Fast Sampling
Xiongye Xiao, Shixuan Li, Luzhe Huang +6
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models hav…
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
Luzhe Huang, Xiongye Xiao, Shixuan Li +4
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, wher…
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Shukai Duan, Heng Ping, Nikos Kanakaris +9
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.…
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta +6
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems…
Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
Xiongye Xiao, Heng Ping, Chenyu Zhou +6
In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Nota…
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
Anzhe Cheng, Zhenkun Wang, Chenzhong Yin +5
Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological impla…
Discovering Malicious Signatures in Software from Structural Interactions
Chenzhong Yin, Hantang Zhang, Mingxi Cheng +4
Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuabl…
PerfRL: A Small Language Model Framework for Efficient Code Optimization
Shukai Duan, Nikos Kanakaris, Xiongye Xiao +8
Code optimization is a challenging task requiring a substantial level of expertise from developers. Nonetheless, this level of human capacity is not sufficient considering the rapi…
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives
Chenzhong Yin, Mingxi Cheng, Xiongye Xiao +4
The collective behavior of a network with heterogeneous, resource-limited information processing units (e.g., group of fish, flock of birds, or network of neurons) demonstrates hig…
Neuro-Inspired Hierarchical Multimodal Learning
Xiongye Xiao, Gengshuo Liu, Gaurav Gupta +6
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration…
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Xiongye Xiao, Defu Cao, Ruochen Yang +5
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve…
Multiwavelet-based Operator Learning for Differential Equations
Gaurav Gupta, Xiongye Xiao, Paul Bogdan
The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space. Towards this end, we introduce a \te…