Search Results for author:
Found 19 papers, 5 papers with code
Differentially Private Sequential Learning
Learning with continuous signals in the private regime is more efficient, as smooth randomized response enhances the log-likelihood ratio over time, improving information aggregation.
Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics
Neurological disorders represent significant global health challenges, driving the advancement of brain signal analysis methods.
SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding
Brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is a popular paradigm for its simplicity and high information transfer rate (ITR).
Embodied Scene Understanding for Vision Language Models via MetaVQA
Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications.
EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases.
Imaging foundation model for universal enhancement of non-ideal measurement CT
However, the resulting trade-offs degrade image quality, limiting clinical acceptability.
Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning
Multimodal sentiment analysis aims to learn representations from different modalities to identify human emotions.
NeuralBeta: Estimating Beta Using Deep Learning
Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging.
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent.
Generative AI to Generate Test Data Generators
The results show that LLMs can successfully generate realistic test data generators in a wide range of domains at all three levels of integrability.
Step-size Optimization for Continual Learning
We conclude by suggesting that combining both approaches could be a promising future direction to improve the performance of neural networks in continual learning.
Text-Guided Texturing by Synchronized Multi-View Diffusion
In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency.
P2CADNet: An End-to-End Reconstruction Network for Parametric 3D CAD Model from Point Clouds
Computer Aided Design (CAD), especially the feature-based parametric CAD, plays an important role in modern industry and society.
Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance
Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.
Teacher Forcing Recovers Reward Functions for Text Generation
We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property
Therefore, a hypergraph neural network model based on dual channel convolution is proposed.
Large Scale Visual Food Recognition
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.
Data-driven surrogate modelling and benchmarking for process equipment
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization.
