Search Results for author:
Found 13 papers, 3 papers with code
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
In this work, two very prominent GAN-based architectures were evaluated in the context of private time series classification.
Privacy Meets Explainability: A Comprehensive Impact Benchmark
This work is the first to investigate the impact of private learning techniques on generated explanations for DL-based models.
Explainable Artificial Intelligence (XAI)
Privacy Preserving
+1
Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis
However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects.
TimeREISE: Time-series Randomized Evolving Input Sample Explanation
Deep neural networks are one of the most successful classifiers across different domains.
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods
However, due to the lack of transparency the use of these networks is hampered in the areas with safety critical areas.
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders.
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
The classification of time-series data is pivotal for streaming data and comes with many challenges.
ImpactCite: An XLNet-based method for Citation Impact Analysis
Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
Ranked #1 on
Citation Intent Classification
on SciCite
(using extra training data)
Interpreting Deep Models through the Lens of Data
Identification of input data points relevant for the classifier (i. e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging.
P2ExNet: Patch-based Prototype Explanation Network
Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks.
TSInsight: A local-global attribution framework for interpretability in time-series data
We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty.
SentiCite: An Approach for Publication Sentiment Analysis
This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e. g., reference to a dataset, reading reference.
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
This indicates a vital gap between the explainability provided by the systems and the novice user.
