This workshop aims to bring together researchers in stochastic analysis, statistics and theoretical machine learning for an exchange of ideas at the forefront of the field. The
In this presentation, Yutong will introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, she will define a common format, “visual sentences”, in which she can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions without needing any meta-knowledge beyond the pixels.
This is a two-part talk series with an hours break in between on Large Language Models (LLMs) intended for non-specialists. I intend to explain the current paradigm, its main unsolved problems, and also provide links to potential interdisciplinary research areas.
We are excited to invite you to the I-X Breaking Topics in AI conference supported by Schmidt Futures
In this presentation, Leandro will share several accomplishments of the BigCode project, an open-scientific collaboration working on the responsible development and use of LLMs for code generation.
Language agents are emerging AI systems that use large language models (LLMs) to interact with the world. While various methods and demos have been developed, it is often hard to systematically understand or evaluate them.
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output – a challenge that is known as credit assignment. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning.
