Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

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A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

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The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

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Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

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Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

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Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

Analysis and classification of EEG signals using probabilistic models for brain computer interfaces

Chattez avec Graph Search

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Diffusion Stochastic Optimization for Min-Max Problems

Sparse autoregressive neural networks for classical spin systems

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

On the added value of sequential deep learning for the upscaling of evapotranspiration

Text-to-Microstructure Generation Using Generative Deep Learning

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Robust machine learning for neuroscientific inference

Automated Generation of Emotion-Conveying Sounds for Robots

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction