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Found 10 papers, 6 papers with code
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Parametric Augmentation for Time Series Contrastive Learning
In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format.
PAC Learnability under Explanation-Preserving Graph Perturbations
It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
DyExplainer: Explainable Dynamic Graph Neural Networks
Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships.
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data.
Personalized Federated Learning via Heterogeneous Modular Networks
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.
Multi-Relevance Transfer Learning
In this paper, we propose a novel and effective approach called Multi-Relevance Transfer Learning (MRTL) for this purpose, which can simultaneously transfer different knowledge from the source and exploits the shared common latent factors between target domains.
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing.
