Search Results for author:
Found 9 papers, 2 papers with code
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps.
Robust Diffusion Models for Adversarial Purification
We propose a novel robust reverse process with adversarial guidance, which is independent of given pre-trained DMs and avoids retraining or fine-tuning the DMs.
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization
The deep neural networks are known to be vulnerable to well-designed adversarial attacks.
Efficient Nonparametric Tensor Decomposition for Binary and Count Data
Finally, to address the computational issue of GPs, we enhance the model by incorporating sparse orthogonal variational inference of inducing points, which offers a more effective covariance approximation within GPs and stochastic natural gradient updates for nonparametric models.
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs.
Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data.
Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network
It is thus crucial to develop automated models for the detection of abnormalities in EEGs related to epilepsy.
YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks With Clustering Small Dataset
However, it is difficult to apply class conditional GANs when the amount of original data is small and when a clear class is not given, such as a yuruchara image.
Generalized Gaussian Kernel Adaptive Filtering
The present paper proposes generalized Gaussian kernel adaptive filtering, where the kernel parameters are adaptive and data-driven.
