Hattie Zhou
Hattie Zhou is a data scientist with Uber's Marketing Analytics team.
Engineering Blog Articles
Introducing LCA: Loss Change Allocation for Neural Network Training
Uber AI Labs proposes Loss Change Allocation (LCA), a new method that provides a rich window into the neural network training process.
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.
Research Papers
LCA: Loss Change Allocation for Neural Network Training
J. Lan, R. Liu, H. Zhou, J. Yosinski
Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into training called Loss Change Allocation (LCA), in which credit for changes to the network loss is conservatively partitioned to the parameters. [...] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into this high-dimensional, dynamic process. We propose a new window into training called Loss Change Allocation (LCA), in which credit for changes to the network loss is conservatively partitioned to the parameters. [...] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
H. Zhou, J. Lan, R. Liu, J. Yosinski
Optical Character Recognition (OCR) approaches have been widely advanced in recent years thanks to the resurgence of deep learning. The state-of-the-art models are mainly trained on the datasets consisting of the constrained scenes. Detecting and recognizing text from the real-world images remains a technical challenge. [...] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Optical Character Recognition (OCR) approaches have been widely advanced in recent years thanks to the resurgence of deep learning. The state-of-the-art models are mainly trained on the datasets consisting of the constrained scenes. Detecting and recognizing text from the real-world images remains a technical challenge. [...] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
