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Found 9 papers, 2 papers with code
Hope Speech Detection on Social Media Platforms
Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded.
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations
The ability to quickly examine combinations of the most promising gradient directions as well as to incorporate additional user-defined constraints allows us to generate multiple counterfactual explanations that are sparse, realistic, and robust to input manipulations.
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
In this paper, we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at learning fairer, more explainable/interpretable models that can also rapidly adjust to drifts in both the accuracy and the fairness of a classifier.
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches?
FaiR-N: Fair and Robust Neural Networks for Structured Data
Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains.
Explainable Machine Learning in Deployment
Yet there is little understanding of how organizations use these methods in practice.
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Given a model and an input instance, CERTIFAI uses a custom genetic algorithm to generate counterfactuals: instances close to the input that change the prediction of the model.
Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play?
We construct an elaborate features space for our system by combining conditional rules from the grammar \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, semantic relations between the compound components from a lexical database \textit{Amarakoṣa} and linguistic structures from the data using Adaptor Grammars.
Significance of the levels of spectral valleys with application to front/back distinction of vowel sounds
An objective critical distance (OCD) has been defined as that spacing between adjacent formants, when the level of the valley between them reaches the mean spectral level.
