Search Results for author:
Found 16 papers, 3 papers with code
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection
We evaluate our end-to-end framework on both simulated IDS data and a real-world ISP dataset, demonstrating significant improvements in intrusion detection performance.
Robust Multiple Description Neural Video Codec with Masked Transformer for Dynamic and Noisy Networks
We propose a novel MDC video codec, NeuralMDC, demonstrating how bidirectional transformers trained for masked token prediction can vastly simplify the design of MDC video codec.
Disentangled Generative Graph Representation Learning
This oversight results in non-robustness and a lack of explainability.
MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to learn semantic primitives (attributes and objects) from seen compositions and recognize unseen attribute-object compositions.
What Large Language Models Know and What People Think They Know
Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models' actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers.
Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its improve boundary prediction.
Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Objective: Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction.
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts
Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications.
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation
Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos.
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.
Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery
To date, there are no effective treatments for most neurodegenerative diseases.
Physics-Guided Deep Neural Networks for Power Flow Analysis
Nevertheless, prior data-driven approaches suffer from poor performance and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems.
