1.5k citations · 4.4k across the 17 of their papers we have counts for
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Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models
Luca Eyring, Shyamgopal Karthik, Alexey Dosovitskiy +2
The new paradigm of test-time scaling has yielded remarkable breakthroughs in Large Language Models (LLMs) (e.g. reasoning models) and in generative vision models, allowing models…
Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner +5
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learn…
Motion Perception in Reinforcement Learning with Dynamic Objects
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun +1
In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motio…
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun +1
Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear functi…
Semi-parametric Topological Memory for Navigation
Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun
We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological…
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
Manolis Savva, Angel X. Chang, Alexey Dosovitskiy +2
We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large…