Large language models for robotics: Opportunities, challenges, and perspectives
Large language models (LLMs) have undergone significant expansion and have been
increasingly integrated across various domains. Notably, in the realm of robot task planning …
increasingly integrated across various domains. Notably, in the realm of robot task planning …
Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text
The conversion of brain activity into text using electroencephalography (EEG) has gained
significant traction in recent years. Many researchers are working to develop new models to …
significant traction in recent years. Many researchers are working to develop new models to …
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
The field of general time series analysis has recently begun to explore unified modeling,
where a common architectural backbone can be retrained on a specific task for a specific …
where a common architectural backbone can be retrained on a specific task for a specific …
Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding
Previous research has demonstrated the potential of using pre-trained language models for
decoding open vocabulary Electroencephalography (EEG) signals captured through a non …
decoding open vocabulary Electroencephalography (EEG) signals captured through a non …
Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy
W Mai, J Zhang, P Fang, Z Zhang - arXiv preprint arXiv:2401.00430, 2023 - arxiv.org
In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal
synthesis technologies (eg, text-to-image, text-to-video, text-to-audio, etc) are gradually …
synthesis technologies (eg, text-to-image, text-to-video, text-to-audio, etc) are gradually …
Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain–Computer Interface for Decoding Imagined Syllables
Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an
external device without the involvement of the motor system, relying exclusively on neural …
external device without the involvement of the motor system, relying exclusively on neural …
Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM
Decoding language information from brain signals represents a vital research area within
brain-computer interfaces, particularly in the context of deciphering the semantic information …
brain-computer interfaces, particularly in the context of deciphering the semantic information …
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals
Invasive brain-computer interfaces have garnered significant attention due to their high
performance. The current intracranial stereoElectroEncephaloGraphy (sEEG) foundation …
performance. The current intracranial stereoElectroEncephaloGraphy (sEEG) foundation …
Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
B Yu, S Zhang - arXiv preprint arXiv:2312.07213, 2023 - arxiv.org
The continuous development of artificial intelligence has a profound impact on biomedical
research and other fields. Brain-inspired computing is an important intersection of …
research and other fields. Brain-inspired computing is an important intersection of …
Enhancing Traffic Prediction with Textual Data Using Large Language Models
X Huang - arXiv preprint arXiv:2405.06719, 2024 - arxiv.org
Traffic prediction is pivotal for rational transportation supply scheduling and allocation.
Existing researches into short-term traffic prediction, however, face challenges in adequately …
Existing researches into short-term traffic prediction, however, face challenges in adequately …