Paper: https://link.springer.com/article/10.1007/s10994-017-5633-9

Discussion led by Peter Husisian & Julia Allen, Intelligent Systems Subteam

Paper Objective

  • Utilize the incredible speed-up (800 billion factor) in mixed-integer optimization enabled by hardware improvements (Although they were deemed too expensive to be practical in the past, the paper shows that these methods are practically solvable on real-world…


Paper: https://openaccess.thecvf.com/content_CVPR_2019/papers/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.pdf

Discussion led by Stephen Tse & Felix Hohne, Intelligent Systems Subteam

Paper Objective

It is often implicitly assumed that models which perform well on ImageNet would perform better in other CV tasks as well. This paper looks to empirically investigate if it is true that models trained on ImageNet perform…


Paper: https://arxiv.org/pdf/2008.10546.pdf

Discussion led by Phillip Si & Melinda Fang, Intelligent Systems subteam

Objectives of the Paper

Three sentence paper summary: Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets. …


Paper: https://arxiv.org/pdf/2010.11929.pdf

Discussion led by Victor Butoi & Cora Wu, Intelligent Systems subteam

Objectives of the Paper

What problem is the paper tackling?
Image Recognition at Scale tries to tackle the issue of applying Transformer architecture to Computer Vision tasks to lessen the field’s heavy reliance on CNNs. …


Paper: https://arxiv.org/pdf/2004.10934.pdf

Discussion led by Katie Yang, Evelyn Wu, and Wendy Huang, Intelligent Systems subteam

Objectives of the Paper

  • Develop a real-time object detection that can be trained on a standard GPU. They explore the performance and speed tradeoffs of appending new features such as mosaic data augmentation, Mish-activation, and DropBlock regularization of the…


Paper: https://arxiv.org/pdf/1704.04861.pdf

Discussion led by Felix Hohne & Evelyn Wu, Intelligent Systems subteam

Introduction

In order to increase model accuracy, data scientists often continuously increase the size of models. However, this comes at the expense of efficiency and computational hardware requirements. As a result, these oversized models may become unsuitable for…


Paper: https://arxiv.org/pdf/1803.03635.pdf

Discussion led by Peter Husisian & Phillip Si, Intelligent Systems subteam

Objectives/ Goals of the Paper

What problem is the paper tackling?

In Machine Learning, pruning techniques that remove unnecessary parameters are used in neural networks to improve speed and decrease model size without materially affecting accuracy. Earlier work has demonstrated that removing up to 90% of model parameters…


Paper: https://arxiv.org/pdf/1906.06725.pdf

Discussion led by Cora Wu & Winnie Chow, Intelligent Systems subteam

Objectives / Goals of the Paper

The main objective of this paper is to develop the foundations for personalized persuasive conversational agents to change people’s opinions and actions for social good. In the paper, the authors create a classifier to predict persuasive strategies…


Discussion led by Katie Yang & Stephen Tse, Intelligent Systems subteam

Objectives / Goals of the Paper

NLP models have become increasingly complex in the last decade, with GPT, GPT-2, BERT, and GPT-3 using ever more parameters and computational power. GPT-3, the current SOTA model from Open-AI, has 185 billion parameters and cost $5 million to…


By Danny Yang, Insights Subteam

One of the biggest challenges for people new to data science is finding the right dataset to use. There are a lot of online sources for data these days: from online collections found on sites like Kaggle to government and nonprofit open data APIs. …

Cornell Data Science

Cornell Data Science is an engineering project team @Cornell that seeks to prepare students for a career in data science.

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