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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Hung Viet Pham
About me
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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
Published in ICSE, 2019
We propose CRADLE, a new approach that focuses on finding and localizing bugs in DL software libraries.
Recommended citation: Hung Viet Pham, Thibaud Lutellier, Weizhen Qi, Lin Tan. (2019). "CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries." ICSE. PDF
Problems and opportunities in training deep learning software systems: an analysis of variance
Published in ASE, 2020
This work is the first to study the variance of DL systems and the awareness of this variance among researchers and practitioners.
Recommended citation: Hung Viet Pham, Shangshu Qian, Jiannan Wang, Thibaud Lutellier, Jonathan Rosenthal, Lin Tan, Yaoliang Yu, Nachiappan Nagappan. (2020). "Problems and opportunities in training deep learning software systems: an analysis of variance." ASE. PDF
talks
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries
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We propose CRADLE, a new approach that focuses on finding and localizing bugs in DL software libraries. CRADLE (1) performs cross-implementation inconsistency checking to detect bugs in DL libraries, and (2) leverages anomaly propagation tracking and analysis to localize faulty functions in DL libraries that cause the bugs. We evaluate CRADLE on three libraries (TensorFlow, CNTK, and Theano), 11 datasets (including ImageNet, MNIST, and KGS Go game), and 30 pre-trained models. CRADLE detects 12 bugs and 104 unique inconsistencies, and highlights functions relevant to the causes of inconsistencies for all 104 unique inconsistencies.
Problems and opportunities in training deep learning software systems: an analysis of variance
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Deep learning (DL) training algorithms utilize nondeterminism to improve models’ accuracy and training efficiency. Hence, multiple identical training runs (e.g., identical training data, algorithm, and network) produce different models with different accuracies and training times. In addition to these algorithmic factors, DL libraries (e.g., TensorFlow and cuDNN) introduce additional variance (referred to as implementation-level variance) due to parallelism, optimization, and floating-point computation.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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