2 citations · 3 across the 14 of their papers we have counts for
17 papers
Rollback-Free Stable Brick Structures Generation
Chenhui Xu, Ziyue Bai, Fuxun Yu +2
While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements of gravity and interconnecti…
Chain-of-Adaptation: Surgical Vision-Language Adaptation with Reinforcement Learning
Jiajie Li, Chenhui Xu, Meihuan Liu +1
Conventional fine-tuning on domain-specific datasets can inadvertently alter a model's pretrained multimodal priors, leading to reduced generalization. To address this, we propose…
Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards
Chenhui Xu, Fuxun Yu, Michael J. Bianco +15
Training robust reasoning vision-language models (VLMs) in rare domains (such as geospatial) is fundamentally constrained by supervision scarcity. While raw geospatial imagery is a…
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation
Dancheng Liu, Amir Nassereldine, Chenhui Xu +1
Whisper's robust performance in automatic speech recognition (ASR) is often attributed to its massive 680k-hour training set, an impractical scale for most researchers. In this wor…
FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks
Chenhui Xu, Dancheng Liu, Amir Nassereldine +1
Physics Informed Neural Networks (PINNs) often exhibit failure modes in which the PDE residual loss converges while the solution error stays large, a phenomenon traditionally blame…
Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability
Chenhui Xu, Dancheng Liu, Jiajie Li +3
Recent advancements in cognitive science and multi-round reasoning techniques for Large Language Models (LLMs) suggest that iterative thinking processes improve problem-solving per…
Driving Through Uncertainty: Risk-Averse Control with LLM Commonsense for Autonomous Driving under Perception Deficits
Yuting Hu, Chenhui Xu, Ruiyang Qin +4
Partial perception deficits can compromise autonomous vehicle safety by disrupting environmental understanding. Existing protocols typically default to entirely risk-avoidant actio…
Sub-Sequential Physics-Informed Learning with State Space Model
Chenhui Xu, Dancheng Liu, Yuting Hu +4
Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure mod…
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Jiajie Li, Brian R Quaranto, Chenhui Xu +5
We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and obj…
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge
Ruiyang Qin, Dancheng Liu, Gelei Xu +7
The combination of Large Language Models (LLM) and Automatic Speech Recognition (ASR), when deployed on edge devices (called edge ASR-LLM), can serve as a powerful personalized ass…
Large Language Models have Intrinsic Self-Correction Ability
Dancheng Liu, Amir Nassereldine, Ziming Yang +8
Large language models (LLMs) have attracted significant attention for their exceptional abilities in various natural language processing tasks, but they suffer from hallucinations…
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices
Amir Nassereldine, Dancheng Liu, Chenhui Xu +3
Edge-based automatic speech recognition (ASR) technologies are increasingly prevalent in the development of intelligent and personalized assistants. However, resource-constrained A…
QuadraNet V2: Efficient and Sustainable Training of High-Order Neural Networks with Quadratic Adaptation
Chenhui Xu, Xinyao Wang, Fuxun Yu +2
Machine learning is evolving towards high-order models that necessitate pre-training on extensive datasets, a process associated with significant overheads. Traditional models, des…
Infinite-Dimensional Feature Interaction
Chenhui Xu, Fuxun Yu, Maoliang Li +4
The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e.g., width, depth), but overlooked the feature interaction s…
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Chenhui Xu, Fuxun Yu, Zirui Xu +2
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequentl…
Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
Dancheng Liu, Chenhui Xu, Jiajie Li +2
For collaborative inference through a cloud computing platform, it is sometimes essential for the client to shield its sensitive information from the cloud provider. In this paper,…
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks
Chenhui Xu, Fuxun Yu, Zirui Xu +3
Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features. And there emerged a varie…