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Predicting Memory-Based Consumer Choices From Recall and Preferences

Published in Association of Consumer Research, 2018

We present a two-stage model of consumer brand choice using behavioral measures of both brand memory and preference. This model outperforms standard models accounting for preferences alone in predicting memory-based choices, and also sheds new light on the mechanism by which brand memory is translated into purchase behavior.

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Predicting Consumer Brand Recall and Choice Using Large-Scale Text Corpora

Published in Association of Consumer Research, 2018

This paper aimed to use the word2vec models to see if we could predict which brands (Coke, Pepsi) people think of given a category (Soft Drinks). It turns out the dot product of the word2vec for the brand and category correlates with recall rate with a roughly power law relation.

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Checkmate: Breaking the memory wall with optimal tensor rematerialization

Published in Proceedings of Machine Learning and Systems, 2020

Modern neural networks are increasingly bottlenecked by the limited capacity of on-device GPU memory. Prior work explores dropping activations as a strategy to scale to larger neural networks with fixed memory. However, these heuristics assume uniform cost per layer and only consider simple linear chain architectures, limiting their usability. In this paper, we formalize the problem of trading-off computation time and memory requirements for DNN training as the tensor rematerialization optimization problem. We develop a new system to optimally solve the problem in reasonable times (under an hour) using off-the-shelf MILP solvers.

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Self-supervised pretraining improves self-supervised pretraining

Published in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to data augmentation. Prior work demonstrates that models pretrained on datasets dissimilar to their target data, such as chest X-ray models trained on ImageNet, underperform models trained from scratch. Users that lack the resources to pretrain must use existing models with lower performance. This paper explores Hierarchical PreTraining (HPT), which decreases convergence time and improves accuracy by initializing the pretraining process with an existing pretrained model. Through experimentation on 16 diverse vision datasets, we show HPT converges up to 80x faster, improves accuracy across tasks, and improves the robustness of the self-supervised pretraining process to changes in the image augmentation policy or amount of pretraining data. Taken together, HPT provides a simple framework for obtaining better pretrained representations with less computational resources.

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teaching

Computer Science 70 at UC Berkeley

Undergraduate course, , 2022

I taught 70 for either as course staff for CSM for a long time at Berkeley. The reason I never switched to a different course is that I felt 70 allowed me the greatest opportunity to help students. The difficulty of the course paired with the fact that it was usually the last course students took for the GPA cutoff meant that students were quite stressed.