Uploaded February 2024 | Updated September 2026, 3 weeks ago
Allen School Distinguished Lecture Series
Title: Beyond Test Accuracies for Studying Deep Neural Networks
Speaker: Kyunghyun Cho (New York University | Genentech)
Host: Yejin Choi
Date: February 8, 2024
Abstract: Already in 2015, Leon Bottou discussed the prevalence and end of the training/test experimental paradigm in machine learning. The machine learning community has however continued to stick to this paradigm until now (2023), relying almost entirely and exclusively on the test-set accuracy, which is a rough proxy to the true quality of a machine learning system we want to measure. There are however many aspects in building a machine learning system that require more attention. Specifically, I will discuss three such aspects in this talk; (1) model assumption and construction, (2) optimization and (3) inference. For model assumption and construction, I will discuss our recent work on generative multitask learning and incidental correlation in multimodal learning. For optimization, I will talk about how we can systematically study and investigate learning trajectories. Finally for inference, I will lay out two consistencies that must be satisfied by a large-scale language model and demonstrate that most of the language models do not fully satisfy such consistencies.
Bio: Kyunghyun Cho is a professor of computer science and data science at New York University and a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He is also a CIFAR Fellow of Learning in Machines & Brains and an Associate Member of the National Academy of Engineering of Korea. He served as a (co-)Program Chair of ICLR 2020, NeurIPS 2022 and ICML 2022. He is also a founding co-Editor-in-Chief of the Transactions on Machine Learning Research (TMLR). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
This video is closed captioned.
Allen School Distinguished Lecture Series
Title: Beyond Test Accuracies for Studying Deep Neural Networks
Speaker: Kyunghyun Cho (New York University | Genentech)
Host: Yejin Choi
Date: February 8, 2024
Abstract: Already in 2015, Leon Bottou discussed the prevalence and end of the training/test experimental paradigm in machine learning. The machine learning community has however continued to stick to this paradigm until now (2023), relying almost entirely and exclusively on the test-set accuracy, which is a rough proxy to the true quality of a machine learning system we want to measure. There are however many aspects in building a machine learning system that require more attention. Specifically, I will discuss three such aspects in this talk; (1) model assumption and construction, (2) optimization and (3) inference. For model assumption and construction, I will discuss our recent work on generative multitask learning and incidental correlation in multimodal learning. For optimization, I will talk about how we can systematically study and investigate learning trajectories. Finally for inference, I will lay out two consistencies that must be satisfied by a large-scale language model and demonstrate that most of the language models do not fully satisfy such consistencies.
Bio: Kyunghyun Cho is a professor of computer science and data science at New York University and a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He is also a CIFAR Fellow of Learning in Machines & Brains and an Associate Member of the National Academy of Engineering of Korea. He served as a (co-)Program Chair of ICLR 2020, NeurIPS 2022 and ICML 2022. He is also a founding co-Editor-in-Chief of the Transactions on Machine Learning Research (TMLR). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
This video is closed captioned.
![[Audio Descriptions] I Am CSE: Kyle Johnson](https://i.ytimg.com/vi/dYksN04tkCA/mqdefault.jpg "[Audio Descriptions] I Am CSE: Kyle Johnson
Allen School graduate student Kyle Johnson of the UW’s Mobile Intelligence Lab and Autonomous Insect Robotics Lab, shares his work on origami microfliers, tiny battery-free robots inspired by nature that can be used for environmental monitoring, as well as his role in A Vision for Engineering Leadership and Access (AVELA) focused on engaging underserved youth.
This video is closed captioned.
A version of this video without audio descriptions is available here: https://youtu.be/tyOcyn2LuIw.")
