Search
Search Results
-
Boosting court judgment prediction and explanation using legal entities
The automatic prediction of court case judgments using Deep Learning and Natural Language Processing is challenged by the variety of norms and...
-
Causal inference in the medical domain: a survey
Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through...
-
Visual explanation and robustness assessment optimization of saliency maps for image classification
For image classification using Deep Learning, applying visual explanations allows end-users to understand better the basis of model decisions in the...
-
Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network
Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood...
-
Shoulder rehabilitation: a neuro-fuzzy inference approach to recovery prediction
This study proposes a system for predicting the recovery status of patients with shoulder damage by estimating the results of the Disabilities of the...
-
Accelerating BERT inference with GPU-efficient exit prediction
BERT is a representative pre-trained language model that has drawn extensive attention for significant improvements in downstream Natural Language...
-
DExT: Detector Explanation Toolkit
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented...
-
RouteExplainer: An Explanation Framework for Vehicle Routing Problem
The Vehicle Routing Problem (VRP) is a widely studied combinatorial optimization problem and has been applied to various practical problems. While...
-
Inference to the Stable Explanations
The process of explaining a piece of evidence by constructing a set of assumptions that are a good explanation for that evidence is ubiquitous in...
-
MANet: Mixed Attention Network for Visual Explanation
Various visual explanation methods, such as CAM and Grad-CAM, have been proposed to visualize and interpret predictions made by CNNs. Recent efforts...
-
CMed-Baichuan: Task Explanation-Enhanced Prompt Method on PromptCBLUE Benchmark
Large Language Models (LLMs) have received widespread attention from academia and industry for their excellent performance on NLP tasks. Due to the...
-
Explanation-Guided Minimum Adversarial Attack
Machine learning has been tremendously successful in various fields, rang-ing from image classification to natural language processing. Despite it...
-
Explanation-based data-free model extraction attacks
Deep learning (DL) has dramatically pushed the previous limits of various tasks, ranging from computer vision to natural language processing. Despite...
-
What Boosts Fake News Dissemination on Social Media? A Causal Inference View
There has been an upward trend of fake news propagation on social media. To solve the fake news propagation problem, it is crucial to understand...
-
A universal approach for multi-model schema inference
The variety feature of Big Data, represented by multi-model data , has brought a new dimension of complexity to all aspects of data management. The...
-
Causal Inference in Data Analysis with Applications to Fairness and Explanations
Causal inference is a fundamental concept that goes beyond simple correlation and model-based prediction analysis, and is highly relevant in domains...
-
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of black-box Algorithmic rankers
Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high...
-
Bayesian inference of transition matrices from incomplete graph data with a topological prior
Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is...
-
Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that...
-
A novel inference paradigm based on multi-view prototypes for one-shot semantic segmentation
AbstractOne-shot semantic segmentation approaches aim to learn a meta-learning framework from seen classes with annotated samples, which can be...
