1.4k citations · 4.2k across the 73 of their papers we have counts for
79 papers
SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts
Shanglin Li, Shiwen Chu, Okan Koç +5
Electroencephalography (EEG) based brain-computer interfaces enable direct brain-to-device communication for applications such as rehabilitation and communication. However, their p…
Exponential-Family Tensor Completion via Nonconvex Dual Total-Variation Regularization
Wenfei Cao, Yang Chen, Qibin Zhao +2
With the emergence of various tensor data, tensor completion from partial measurements has attracted widespread attention in data science and signal processing. Total Variation (TV…
EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
Runhe Zhou, Shanglin Li, Guanxiang Huang +5
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential…
Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold
Jian Xu, Wei Chen, Shigui Li +4
Score-based diffusion can be defined intrinsically on the manifold of quantum pure states, with the Fubini--Study metric, but no closed-form transition density…
One-Step Score-Based Density Ratio Estimation
Wei Chen, Qibin Zhao, John Paisley +2
Density ratio estimation (DRE) is a useful tool for quantifying discrepancies between probability distributions, but existing approaches often involve a trade-off between estimatio…
Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner
Wei Chen, Yubing Wu, Junmei Yang +5
Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based methods also suppress the chosen response when they…
Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties
Takanobu Furuhashi, Hiroki Kuroda, Masahiro Yukawa +3
We propose two nonconvex regularization methods, LogLOP-l2/l1 and AdaLOP-l2/l1, for recovering block-sparse signals with unknown block partitions. These methods address the underes…
Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Guang Lin, Toshihisa Tanaka, Qibin Zhao
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and sub…
HEEGNet: Hyperbolic Embeddings for EEG
Shanglin Li, Shiwen Chu, Okan Koç +4
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. Howeve…
Calibrating Uncertainty for Zero-Shot Adversarial CLIP
Wenjing Lu, Zerui Tao, Yuning Qiu +3
CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work primarily matches predicted logits between cl…
Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement
Jian Xu, Wei Chen, Shigui Li +3
Retinex-based low-light image enhancement benefits from separating reflectance and illumination, yet recent generative approaches often rely on iterative sampling and are difficult…
When, What, and How: Rethinking Retrieval-Enhanced Speculative Decoding
Min Fang, Zhihui Fu, Qibin Zhao +1
Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable spee…
Efficient Low Rank Attention for Long-Context Inference in Large Language Models
Tenghui Li, Guoxu Zhou, Xuyang Zhao +2
As the length of input text increases, the key-value (KV) cache in LLMs imposes prohibitive GPU memory costs and limits long-context inference on resource constrained devices. Exis…
DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding
Guanghao Li, Zhihui Fu, Min Fang +4
As large language models (LLMs) scale up, accuracy improves, but the autoregressive (AR) nature of decoding increases latency since each token requires a serial forward pass. Specu…
Diffusion Bridge Variational Inference for Deep Gaussian Processes
Jian Xu, Qibin Zhao, John Paisley +1
Deep Gaussian processes (DGPs) enable expressive hierarchical Bayesian modeling but pose substantial challenges for posterior inference, especially over inducing variables. Denoisi…
Diffusion Secant Alignment for Score-Based Density Ratio Estimation
Wei Chen, Shigui Li, Jiacheng Li +6
Estimating density ratios has become increasingly important with the recent rise of score-based and diffusion-inspired methods. However, current tangent-based approaches rely on a…
QuProFS: An Evolutionary Training-free Approach to Efficient Quantum Feature Map Search
Yaswitha Gujju, Romain Harang, Chao Li +2
The quest for effective quantum feature maps for data encoding presents significant challenges, particularly due to the flat training landscapes and lengthy training processes asso…
Neural Bridge Processes
Jian Xu, Yican Liu, Delu Zeng +2
Learning stochastic functions from partially observed context-target pairs requires models that are expressive, uncertainty-aware, and strongly conditioned on inputs. Neural Diffus…
Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
Binghua Li, Ziqing Chang, Tong Liang +5
We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance ima…
Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Weichen Dai, Yuxuan Huang, Li Zhu +10
Humans possess a remarkable capacity for spatial cognition, allowing for self-localization even in novel or unfamiliar environments. While hippocampal neurons encoding position and…
WEEP: A Differentiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope
Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1
Sparse regularization is fundamental in signal processing and feature extraction but often relies on non-differentiable penalties, conflicting with gradient-based optimizers. We pr…
NeuronSeek: On Stability and Expressivity of Task-driven Neurons
Hanyu Pei, Jing-Xiao Liao, Qibin Zhao +4
Drawing inspiration from our human brain that designs different neurons for different tasks, recent advances in deep learning have explored modifying a network's neurons to develop…
Domain-Aware Tensor Network Structure Search
Giorgos Iacovides, Wuyang Zhou, Chao Li +2
Tensor networks (TNs) provide efficient representations of high-dimensional data, yet identification of the optimal TN structures, the so called tensor network structure search (TN…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…