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I am a graduate student at the University of California, Santa Cruz, pursuing my master's degree in Computer Science. Previously, I also earned my bachelor's degree in Computer Science at UC Santa Cruz. In addition, I work as a researcher at Professor Jeffrey Flanigan’s JLab. I am currently working on improving the sample complexity rate of deep neural networks. My research interests also lie in machine learning with less data, generalization and memorization of neural networks, and computational neuroscience. |
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These included publications and preprints. |
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We present a dataset filtering approach that uses sets of classifiers, similar to ensembling, to estimate noisy (or non-realizable) examples and exclude them so a faster sample complexity rate is achievable in practice. |
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These include coursework and side projects. |
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I fine-tuned a distilled RoBERTa model as a text classifier on the IMDb Large Movie Review Dataset and a GPT-2 (345M) as a text generator using the proximal policy optimization (PPO) framework. |
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I fine-tuned a pre-trained GPT-2 (774M) model using all research paper titles and abstracts under cs.AI, cs.LG, cs.CL, and cs.CV on arXiv and built a machine learning paper abstract generator. The model is hosted by Hugging Face. This project was the winner of the Image/Text Generation Competition for the course CSE142 Machine Learning in Spring 2020. |
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I fine-tuned a RoBERTa (355M) model using the IMDb dataset and achieved 96.516% accuracy. This project was the winner of the Sentiment Analysis Competition for the course CSE142 Machine Learning in Spring 2020. |
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This is a fork of Jon Barron's website. |