Search Results for author:
Found 17 papers, 2 papers with code
DeFusion: An Effective Decoupling Fusion Network for Multi-Modal Pregnancy Prediction
Temporal embryo images and parental fertility table indicators are both valuable for pregnancy prediction in \textbf{in vitro fertilization embryo transfer} (IVF-ET).
Attribution for Enhanced Explanation with Transferable Adversarial eXploration
The interpretability of deep neural networks is crucial for understanding model decisions in various applications, including computer vision.
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing
GE-AdvGAN, a recent method for transferable adversarial attacks, is based on this principle.
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
Given the essence of adversarial attacks is to impair model integrity with minimal noise on original samples, exploring avenues to maximize the utility of such perturbations is imperative.
Interpretable Robotic Manipulation from Language
Natural language, moreover, serves as the primary medium through which humans acquire new knowledge, presenting a potentially intuitive bridge for translating concepts understandable by humans into formats that can be learned by machines.
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery.
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
Ethical ChatGPT: Concerns, Challenges, and Commandments
Large language models, e. g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population.
Genetic Imitation Learning by Reward Extrapolation
Imitation learning demonstrates remarkable performance in various domains.
Explaining Imitation Learning through Frames
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains.
GANExplainer: GAN-based Graph Neural Networks Explainer
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making.
SFusion: Self-attention based N-to-One Multimodal Fusion Block
To solve this problem, we propose a self-attention based fusion block called SFusion.
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data.
Imitation Learning: Progress, Taxonomies and Challenges
Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors.
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation
The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections.
Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia
In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period.
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach
This method aggregates three main parts that are Back-end Data Model, Neural Net Algorithm including clustering method Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN) for ex- tracting the features and lastly a solid front-end that displays the results to users with an interactive system.
