Yanjun Qi: \

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Systematic prediction of human membrane receptor interactions - Yanjun Qi - ISMB 2010

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Proceedings on Data - Dong-gi Lee, Thomas Gumbsch, Tamim Abdelaal, Yanjun Qi - ECCB 2020

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Yanjun Qi | Joint Learning of Multiple Related Gaussian Graphical Models from Heterogenous Samples

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BAYOYO 2021, Automatic Techniques for Evaluating and Hardening Deep Learning Class... - Yanjun Qi

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TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

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S1-L2-nutshell-Module2: ML by Data or Task (UVa CS 4774 Machine Learning Course)

(24:42)

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

(4:11)

Adversarial-Playground_ A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

(13:6)

Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

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FastSK: Fast Sequence Analysis with Gapped String Kernels

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S5-L24-Clustering-Module1: Basics of Unsupervised Clustering (UVA CS 4774)

(21:43)

S1-L7-RegularizedRegression-Module1: Basics ( UVa CS 4774 Machine Learning Course)

(24:58)

S2-L11-LogisticRegression-Module2: Cross-Entropy ( UVa CS 4774 Machine Learning Course)

(20:40)

S2-L9-BiasVariance-Module3: RemedyControl (UVa CS 4774 Machine Learning Course)

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S0-L1-Introduction-Module2: History/ Breakthroughs (UVa CS 4774 Machine Learning Course)

(29:40)

S4-L23-Ensemble-Module1: Committee of Models and Analysis / Introduction of Ensemble/ VarianceReduct

(35:15)

S2-L11-LogisticRegression-Module1: Bayes Classifier (UVa CS 4774 Machine Learning Course)

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