8 papers · 1 filter
Why Does Agentic Safety Fail to Generalize Across Tasks?
Yonatan Slutzky, Yotam Alexander, Tomer Slor +2
AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen tasks. A major concern in s…
Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data
Yuval Ran-Milo, Yotam Alexander, Shahar Mendel +1
Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought…
Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
Yotam Alexander, Yonatan Slutzky, Yuval Ran-Milo +1
Conventional wisdom attributes the mysterious generalization abilities of overparameterized neural networks to gradient descent (and its variants). The recent volume hypothesis cha…
Provable Benefits of Complex Parameterizations for Structured State Space Models
Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik +3
Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most no…
The Implicit Bias of Structured State Space Models Can Be Poisoned With Clean Labels
Yonatan Slutzky, Yotam Alexander, Noam Razin +1
Neural networks are powered by an implicit bias: a tendency of gradient descent to fit training data in a way that generalizes to unseen data. A recent class of neural network mode…
Lecture Notes on Linear Neural Networks: A Tale of Optimization and Generalization in Deep Learning
Nadav Cohen, Noam Razin
These notes are based on a lecture delivered by NC on March 2021, as part of an advanced course in Princeton University on the mathematical understanding of deep learning. They pre…