Difference between revisions of "User:Snemoto"
From REU@MU
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Currently working with Dr. Perouli on the project: Identifying Appropriate Machine Learning Models for Multi Robot Secure Coordination in a Healthcare Facility. | Currently working with Dr. Perouli on the project: Identifying Appropriate Machine Learning Models for Multi Robot Secure Coordination in a Healthcare Facility. | ||
| − | + | ==Weekly Logs== | |
| + | '''Week 1''' | ||
| + | *Read papers on neural network inversion and HopSkipJump attacks. | ||
| + | *Read abstracts for HumptyDumpty and MemGuard papers | ||
| + | *Attended Orientation | ||
| + | *Filled out pre-REU survey | ||
| + | *Reviewed Python skills | ||
| + | *Learned basics of Pandas and DataFrame manipulation | ||
| + | *Learned basics of creating and evaluating machine learning models | ||
| + | *Learned basic security considerations for applications | ||
| − | * | + | |
| − | * Adversarial Attacks | + | '''Week 2''' |
| − | * | + | *Attended Responsible Conduct of Research Training |
| + | *Attended Technical Writing instruction session | ||
| + | *Completed CITI Modules | ||
| + | *Read papers: | ||
| + | **Procedural Noise Adversarial Examples For Black-Box Attacks on Deep Convolutional Networks | ||
| + | **Certified Robustness to Adversarial Examples with Differential Privacy. | ||
| + | *Read introductions and conclusions for papers: | ||
| + | **Latent Backdoor Attacks on Deep Neural Networks | ||
| + | **Membership Inference Attacks against Adversarially Robust Deep Learning Models | ||
| + | **Seeing isn’t Believing: Towards More Robust Adversarial Attack Against Real World Object Detectors | ||
Revision as of 19:50, 12 June 2020
Shota Nemoto
University: Case Western Reserve University
Currently pursuing a Bachelor's degree in Computer Science
Currently working with Dr. Perouli on the project: Identifying Appropriate Machine Learning Models for Multi Robot Secure Coordination in a Healthcare Facility.
Weekly Logs
Week 1
- Read papers on neural network inversion and HopSkipJump attacks.
- Read abstracts for HumptyDumpty and MemGuard papers
- Attended Orientation
- Filled out pre-REU survey
- Reviewed Python skills
- Learned basics of Pandas and DataFrame manipulation
- Learned basics of creating and evaluating machine learning models
- Learned basic security considerations for applications
Week 2
- Attended Responsible Conduct of Research Training
- Attended Technical Writing instruction session
- Completed CITI Modules
- Read papers:
- Procedural Noise Adversarial Examples For Black-Box Attacks on Deep Convolutional Networks
- Certified Robustness to Adversarial Examples with Differential Privacy.
- Read introductions and conclusions for papers:
- Latent Backdoor Attacks on Deep Neural Networks
- Membership Inference Attacks against Adversarially Robust Deep Learning Models
- Seeing isn’t Believing: Towards More Robust Adversarial Attack Against Real World Object Detectors
