Search Results for author:
Found 8 papers, 2 papers with code
Analysis of Privacy Leakage in Federated Large Language Models
With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs.
On the Limit of Explaining Black-box Temporal Graph Neural Networks
Temporal Graph Neural Network (TGNN) has been receiving a lot of attention recently due to its capability in modeling time-evolving graph-related tasks.
EMaP: Explainable AI with Manifold-based Perturbations
Our study focuses on the impact of perturbing directions on the data topology.
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee
In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions.
An Explainer for Temporal Graph Neural Networks
Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period.
Learning Interpretation with Explainable Knowledge Distillation
Knowledge Distillation (KD) has been considered as a key solution in model compression and acceleration in recent years.
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
In Graph Neural Networks (GNNs), the graph structure is incorporated into the learning of node representations.
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation
Given a classifier's prediction and the corresponding explanation on that prediction, c-Eval is the minimum-distortion perturbation that successfully alters the prediction while keeping the explanation's features unchanged.
