Structured State Space Models for Deep Sequence Modeling (Albert Gu, CMU)
Date: May 26, 2023
(Sorry that the first 2 slides are not recorded, those are motivation slides though.)
Abstract:
This talk will cover recent deep neural networks based on state space models (SSM) starting from S4. I'll go over the core properties and mechanics of SSMs, and discuss the key features of S4 and variants. I'll also focus on discussing the relationship of SSMs with established deep learning models (RNNs, CNNs, Attention) and their corresponding strengths and weaknesses, including potential application areas and promising directions.
Bio:
Albert Gu is an incoming Assistant Professor of Machine Learning at Carnegie Mellon University. His research broadly focuses on theoretical and empirical aspects of deep learning, with a recent focus on new approaches for deep sequence modeling. He completed his PhD at Stanford University under the supervision of Christopher Ré, and is currently working at DeepMind during a gap year.
(Sorry that the first 2 slides are not recorded, those are motivation slides though.)
Abstract:
This talk will cover recent deep neural networks based on state space models (SSM) starting from S4. I'll go over the core properties and mechanics of SSMs, and discuss the key features of S4 and variants. I'll also focus on discussing the relationship of SSMs with established deep learning models (RNNs, CNNs, Attention) and their corresponding strengths and weaknesses, including potential application areas and promising directions.
Bio:
Albert Gu is an incoming Assistant Professor of Machine Learning at Carnegie Mellon University. His research broadly focuses on theoretical and empirical aspects of deep learning, with a recent focus on new approaches for deep sequence modeling. He completed his PhD at Stanford University under the supervision of Christopher Ré, and is currently working at DeepMind during a gap year.
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