Posts by Collection

Portfolio item number 1

Short description of portfolio item number 1

Portfolio item number 2

Short description of portfolio item number 2

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

CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries

Published: May 25, 2019

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

Published: September 23, 2019

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 experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.