5 citations · 12 across the 19 of their papers we have counts for
22 papers
On the Limits of Metacognitive Monitoring in LLMs
Dongqi Han, Yifan Yang, Dongsheng Li
Reliable decisions depend on recognizing when an answer may be wrong. In biological cognition, metacognitive monitoring can dissociate from task performance, raising the question o…
Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction
Juhyeon Park, Yeonwoo Kim, Peter Yongho Kim +5
Foundation models pre-trained on large-scale fMRI datasets have shown strong downstream performance, but at substantial data and computation cost. To investigate how much fMRI-spec…
Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
Mingqing Xiao, Yansen Wang, Dongqi Han +2
Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordinatio…
State-Action Inpainting Diffuser for Continuous Control with Delay
Dongqi Han, Wei Wang, Enze Zhang +1
Signal delay poses a fundamental challenge in continuous control and reinforcement learning (RL) by introducing a temporal gap between interaction and perception. Current solutions…
Improving Diffusion Planners by Self-Supervised Action Gating with Energies
Yuan Lu, Dongqi Han, Yansen Wang +1
Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inco…
EEGChaT: A Transformer-Based Modular Channel Selector for SEEG Analysis
Chen Wang, Yansen Wang, Dongqi Han +2
Analyzing stereoelectroencephalography (SEEG) signals is critical for brain-computer interface (BCI) applications and neuroscience research, yet poses significant challenges due to…
VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL
Kyoungjun Park, Yifan Yang, Juheon Yi +6
The rapid proliferation of AI-generated video necessitates robust detection tools that offer both high accuracy and human-interpretable explanations. While existing MLLM-based dete…
EgoBrain: Synergizing Minds and Eyes For Human Action Understanding
Nie Lin, Yansen Wang, Dongqi Han +5
The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human co…
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs
Zhihe Yang, Xufang Luo, Zilong Wang +4
Reinforcement learning (RL) has become a cornerstone for enhancing the reasoning capabilities of large language models (LLMs), with recent innovations such as Group Relative Policy…
Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key
Zhihe Yang, Xufang Luo, Dongqi Han +2
Hallucination remains a major challenge for Large Vision-Language Models (LVLMs). Direct Preference Optimization (DPO) has gained increasing attention as a simple solution to hallu…
What Makes a Good Diffusion Planner for Decision Making?
Haofei Lu, Dongqi Han, Yifei Shen +1
Diffusion models have recently shown significant potential in solving decision-making problems, particularly in generating behavior plans -- also known as diffusion planning. While…
Habitizing Diffusion Planning for Efficient and Effective Decision Making
Haofei Lu, Yifei Shen, Dongsheng Li +2
Diffusion models have shown great promise in decision-making, also known as diffusion planning. However, the slow inference speeds limit their potential for broader real-world appl…
SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Yimu Zhang, Dongqi Han, Yansen Wang +3
Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling r…
Toward Relative Positional Encoding in Spiking Transformers
Changze Lv, Yansen Wang, Dongqi Han +4
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to…
The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation
Junwen Luo, Chengyong Jiang, Qingyuan Chen +5
Effective visual brain-machine interfaces (BMI) is based on reliable and stable EEG biomarkers. However, traditional adaptive filter-based approaches may suffer from individual var…
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Changze Lv, Dongqi Han, Yansen Wang +3
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying…
Understanding and Improving Training-free Loss-based Diffusion Guidance
Yifei Shen, Xinyang Jiang, Yezhen Wang +3
Adding additional control to pretrained diffusion models has become an increasingly popular research area, with extensive applications in computer vision, reinforcement learning, a…
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Changze Lv, Yansen Wang, Dongqi Han +3
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying S…
Toward Open-ended Embodied Tasks Solving
William Wei Wang, Dongqi Han, Xufang Luo +4
Empowering embodied agents, such as robots, with Artificial Intelligence (AI) has become increasingly important in recent years. A major challenge is task open-endedness. In practi…
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
Jie Lian, Xufang Luo, Caihua Shan +3
Precision medicine tailored to individual patients has gained significant attention in recent times. Machine learning techniques are now employed to process personalized data from…
Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network
Yunhui Zhou, Dongqi Han, Yuguo Yu
Human vision incorporates non-uniform resolution retina, efficient eye movement strategy, and spiking neural network (SNN) to balance the requirements in visual field size, visual…
Habits and goals in synergy: a variational Bayesian framework for behavior
Dongqi Han, Kenji Doya, Dongsheng Li +1
How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be cl…