Tutorials

Zhe’s PhD Defense - Toward Out-Of-Distribution Generalization Of Deep Learning Models

2 minute read

Ph.D. Dissertation Defense by Zhe Wang, Tues., 04/02/24, at 12:00PM (ET) Committee:

Arsh’s PhD Defense - Relational Structure Discovery for Deep Learning

1 minute read

Arshdeep Sekhon’s PhD Defense June 29, 2022.

JackL’s PhD Defense - Modeling interactions with Deep Learning

less than 1 minute read

Ph.D. Dissertation Defense by Jack Lanchantin Tuesday, July 20th, 2021 at 2:00 PM (ET), via Zoom. Committee: Vicente Ordóñez Román, Committee Chair, ...

DrQi’s tutorial talk on “Make Deep Learning Interpretable for Sequential Data Analysis in Biomedicine” (Including our work on DeepChrome - AttentiveChrome - GCNChrome - DeepMotif - DeepVHPPI - MotifTransformer)

less than 1 minute read

I gave a tutorial talk at UVA-VADC Seminar Series 2021 and at monthly NIH Data Science Showcase seminar.

Dr Qi’s Invited Talks on textattack

less than 1 minute read

On June 24th, 2021, I gave an invited talk at the Science Academy Machine Learning Summer School on “TextAttack: Generalizing Adversarial Examples to Natural...

DrQi’s tutorial talk about jointnets at UCLA computational genomics summer school 2019 for extracting connectomes from heterogeneous samples

less than 1 minute read

Here is the slide of my tutorial talk at UCLA computational genomics summer school 2019.

Weilin’s PhD Defense - Improving Robustness of Machine Learning Models using Domain Knowledge

2 minute read

Ph.D. Dissertation Defense by Weilin Xu

DrQi’s Feature Squeezing- Invited Talk at GMU

less than 1 minute read

On April 23 2019, I gave an invited talk at the ARO Invitational Workshop on Foundations of Autonomous Adaptive Cyber Systems

DrQi’s tutorial talk at UVA-CPHG seminar and healthDynamics workshop 2018 for Making Deep Learning Understandable for Genomics

less than 1 minute read

I gave a tutorial talk at UVA-CPHG Seminar Series 2018.

BCB- DrQ’s tutorial talk at ACM-BCB 2018 for Making Deep Learning Understandable for Genomics

less than 1 minute read

Here are the slides of tutorial talk I gave at ACM-BCB 2018.

A Series of Tutorials We wrote to explain the JointS GM tools we built for extracting connectomes from heterogeneous samples

less than 1 minute read

So far, we have released the following Tutorials:

Beilun’s PhD Defense - Fast and Scalable Joint Estimators for Learning Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowledge

1 minute read

PhD Defense Presentation by Beilun Wang Friday, July 20, 2018 at 9:00 am in Rice 242 Committee Members: Mohammad Mahmoody (Chair), Yanjun Qi (Advisor), ...

Qdata

Tutorials

Zhe’s PhD Defense - Toward Out-Of-Distribution Generalization Of Deep Learning Models

Arsh’s PhD Defense - Relational Structure Discovery for Deep Learning

JackL’s PhD Defense - Modeling interactions with Deep Learning

DrQi’s tutorial talk on “Make Deep Learning Interpretable for Sequential Data Analysis in Biomedicine” (Including our work on DeepChrome - AttentiveChrome - GCNChrome - DeepMotif - DeepVHPPI - MotifTransformer)

Dr Qi’s Invited Talks on textattack

DrQi’s tutorial talk about jointnets at UCLA computational genomics summer school 2019 for extracting connectomes from heterogeneous samples

Weilin’s PhD Defense - Improving Robustness of Machine Learning Models using Domain Knowledge

DrQi’s Feature Squeezing- Invited Talk at GMU

DrQi’s tutorial talk at UVA-CPHG seminar and healthDynamics workshop 2018 for Making Deep Learning Understandable for Genomics

BCB- DrQ’s tutorial talk at ACM-BCB 2018 for Making Deep Learning Understandable for Genomics

A Series of Tutorials We wrote to explain the JointS GM tools we built for extracting connectomes from heterogeneous samples

Beilun’s PhD Defense - Fast and Scalable Joint Estimators for Learning Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowledge

Rita’s PhD Defense - Fast and Interpretable Classification of Sequential Data in Biology

DrQ Tutorial Talk about DeepChrome-AttentiveChrome-DeepMotif

DrQ Talk in 2013 about our DeepLearning Works on Protein and BioNLP datasets