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One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Viacheslav Surkov, Chris Wendler, Antonio Mari +5
For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of…
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Mikhail Terekhov, Caglar Gulcehre
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functio…
Learning pure quantum states (almost) without regret
Josep Lumbreras, Mikhail Terekhov, Marco Tomamichel
We initiate the study of sample-optimal quantum state tomography with minimal disturbance to the samples. Can we efficiently learn a precise description of a quantum state through…