Search Results for author:
Found 13 papers, 4 papers with code
MindGPT: Interpreting What You See with Non-invasive Brain Recordings
By doing so, we found that the neural representations of the MindGPT are explainable, which can be used to evaluate the contributions of visual properties to language semantics.
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts.
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach.
LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks
A conversion scheme is proposed to obtain competitive accuracy by mapping trained ANNs' parameters to SNNs with the same structures.
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling.
Benchmarking Unsupervised Anomaly Detection and Localization
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface
In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.
Study on the simulation control of neural network algorithm in thermally coupled distillation
Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution.
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology.
A review of neural network algorithms and their applications in supercritical extraction
Neural network realizes multi-parameter optimization and control by simulating certain mechanisms of the human brain.
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach
We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem.
