Alex Tamkin will be giving a hybrid talk at the NLP Seminar on Friday, Oct 14 from 11am-12pm PST. This talk will be held in person in South Hall 210.
Title: Self-Supervised Learning for the Real World
Abstract: Spearheaded by advances in NLP, machine learning is undergoing a transformative shift towards large, generalist models trained with self-supervised learning (SSL). In this talk, I’ll discuss two challenges lying ahead for this paradigm, as well as some paths towards surmounting them. First, I’ll discuss the problem of task ambiguity. While the space of tasks that models can perform is expanding rapidly, the number of bits (e.g. examples) used to specify the task is shrinking. Given these two opposing forces, how do we ensure that models learn the tasks we intend? I’ll discuss how we can measure the effects of such task ambiguity on humans and language models, as well as work showing how two-way interaction between users and large models can make strides on this problem in NLP and computer vision. Second, I’ll discuss the challenge of domain-agnostic SSL, necessary for realizing the benefits of SSL in high-impact settings such as healthcare, the sciences, and engineering. I’ll present DABS, a novel kind of Domain-Agnostic Benchmark for SSL algorithms, covering data from 12 different fields (e.g. text, genomics, wearable sensors, and particle physics). With DABS, we develop and present the first SSL methods which succeed on such a broad range of modalities.
– Active Learning Helps Pretrained Models Learn the Intended Task
– DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning
– DABS 2.0: Improved Datasets and Algorithms for Universal Self Supervision
– Viewmaker Networks: Learning Views for Unsupervised Representation Learning
Bio: Alex Tamkin is a fifth-year PhD student in Computer Science at Stanford, advised by Noah Goodman and part of the Stanford NLP Group. His research focuses on self-supervised learning, especially in multimodal and domain-general settings. He is a recipient of the Open Philanthropy AI Fellowship.