Search Results for author:
Found 11 papers, 5 papers with code
Contrastive learning-based agent modeling for deep reinforcement learning
However, existing agent modeling approaches typically assume the availability of local observations from other agents (modeled agents) during training or a long observation trajectory for policy adaption.
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision
The proposed BELT method is a generic and efficient framework that bootstraps EEG representation learning using off-the-shelf large-scale pretrained language models (LMs).
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
For speech processing, SpeechPrompt shows its high parameter efficiency and competitive performance on a few speech classification tasks.
Ranked #17 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Cross Task Neural Architecture Search for EEG Signal Classifications
Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature.
Ranked #1 on
EEG 4 classes
on BCI Competition IV 2a
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data.
Ranked #3 on
EEG Left/Right hand
on BCI Competition IV 2a
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction
Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can be potentially generalized to other BCI cases.
Adaptive Initialization Method for K-means Algorithm
(c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets.
Effects of Repetitive SSVEPs on EEG Complexity using Multiscale Inherent Fuzzy Entropy
These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Signal Processing
CT-image Super Resolution Using 3D Convolutional Neural Network
Super resolution (SR) methods based on deep learning have achieved surprising performance in two-dimensional (2D) images.
