An Exploration of Fairness and Dynamics of Learning Systems — Discussion led by Imani Finkley & Rahma Tasnim, Intelligent Systems Subteam Let’s face it: whether it’s a recommended song on Spotify or that ad for a trip to the Bahamas that pops up just when you are ready for vacation, algorithms are making decisions about our lives everyday. …

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…