Uday Kamath

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Effective Automated Feature Construction and Selection for Classification of Biological Sequences

K. De Jong, Uday Kamath, Amarda Shehu, 2014, PloS one.

Introduction to Interpretability and Explainability

Uday Kamath, John Chih Liu, 2021, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning.

Mastering Java Machine Learning

Uday Kamath, Carlotta Domeniconi, Krishna Choppella, 2017 .

Basics of Machine Learning

Uday Kamath, John Chih Liu, James Whitaker, 2019, Deep Learning for NLP and Speech Recognition.

3 Impact of Balanced versus Unbalanced Testing Parti-tions on Model Performance

Uday Kamath, Amarda Shehu, 2018 .

Basics of Deep Learning

John Liu, Uday Kamath, James Whitaker, 2019, Deep Learning for NLP and Speech Recognition.

Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming

Amarda Shehu, Uday Kamath, Daniel Veltri, 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Boosted Mean Shift Clustering

Uday Kamath, Carlotta Domeniconi, Guoji Zhang, 2014, ECML/PKDD.

Parallel boosted clustering

Zenglin Xu, Uday Kamath, Carlotta Domeniconi, 2019, Neurocomputing.

A new methodology for the GP theory toolbox

Kenneth A. De Jong, Uday Kamath, Jeffrey K. Bassett, 2012, GECCO '12.

EML: A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2019, Innovations in Big Data Mining and Embedded Knowledge.

An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Evolutionary Machine Learning Framework for Big Data Sequence Mining

Uday Krishna Kamath, Uday Kamath, 2014 .

Feature and Kernel Evolution for Recognition of Hypersensitive Sites in DNA Sequences

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2010, BIONETICS.

Selecting predictive features for recognition of hypersensitive sites of regulatory genomic sequences with an evolutionary algorithm

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2010, GECCO '10.

Deep learning improves antimicrobial peptide recognition

Amarda Shehu, Uday Kamath, Daniel Veltri, 2018, Bioinform..

Post-Hoc Interpretability and Explanations

Uday Kamath, John Chih Liu, 2021, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning.

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Uday Kamath, John Liu, Uday Kamath, 2021 .

Attention and Memory Augmented Networks

Uday Kamath, James Whitaker, John Liu, 2019, Deep Learning for NLP and Speech Recognition.

Transfer Learning: Scenarios, Self-Taught Learning, and Multitask Learning

Uday Kamath, James Whitaker, John Liu, 2019, Deep Learning for NLP and Speech Recognition.

A novel method to improve recognition of antimicrobial peptides through distal sequence-based features

Amarda Shehu, Uday Kamath, Daniel Veltri, 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Transformers for Machine Learning

Uday Kamath, Wael Emara, Kenneth L. Graham, 2022 .

SAX-EFG: an evolutionary feature generation framework for time series classification

Jessica Lin, Kenneth A. De Jong, Uday Kamath, 2014, GECCO.

Deep Learning for NLP and Speech Recognition

John Liu, Uday Kamath, James Whitaker, 2020, Springer International Publishing.

Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff pdf

Uday Kamath, Christi French, Mitchell Naylor, 2021, ArXiv.

PsychBERT: A Mental Health Language Model for Social Media Mental Health Behavioral Analysis

Uday Kamath, Amarda Shehu, Vedant Vajre, 2021, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

An evolutionary-based approach for feature generation: Eukaryotic promoter recognition

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Model Visualization Techniques and Traditional Interpretable Algorithms

Uday Kamath, John Chih Liu, 2021, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning.

Convolutional Neural Networks

John Liu, Uday Kamath, James Whitaker, 2019, Deep Learning for NLP and Speech Recognition.

Pre-model Interpretability and Explainability

Uday Kamath, John Chih Liu, 2021, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm

Kenneth A. De Jong, Uday Kamath, Carlotta Domeniconi, 2016, Evolutionary Computation.

Theoretical and Empirical Analysis of a Parallel Boosting Algorithm pdf

Kenneth A. De Jong, Uday Kamath, Carlotta Domeniconi, 2015, ArXiv.

Transfer Learning: Domain Adaptation

Uday Kamath, James Whitaker, John Liu, 2019, Deep Learning for NLP and Speech Recognition.

End-to-End Speech Recognition

John Liu, Uday Kamath, James Whitaker, 2019, Deep Learning for NLP and Speech Recognition.

A Two-Stage Evolutionary Approach for Effective Classification of hypersensitive DNA Sequences

Kenneth A. De Jong, Amarda Shehu, Uday Kamath, 2011, J. Bioinform. Comput. Biol..

Explainable Deep Learning

Uday Kamath, John Chih Liu, 2021, Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning.

Text and Speech Basics

Uday Kamath, James Whitaker, John Liu, 2019, Deep Learning for NLP and Speech Recognition.

Using Quantitative Genetics and Phenotypic Traits in Genetic Programming

Uday Kamath, Kenneth de Jong, Jeffrey K. Bassett, 2012 .