Vladlen Koltun
6 papers in the PaperMetrix corpus
Papers by this author
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Exploring Self-Attention for Image Recognition
2020
Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. …
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Habitat 2.0: Training Home Assistants to Rearrange their Habitat
2021 · arXiv (Cornell University)
We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, …
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Online Continual Learning Without the Storage Constraint
2023 · arXiv (Cornell University)
Traditional online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout an agent's lifetime. However, a broad range of real-world applications are primarily constrained by computational …
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Reinforcement Learning for Long-Horizon Interactive LLM Agents
2025 · arXiv (Cornell University)
Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform tasks in response to user requests. While IDAs powered by instruction-tuned large language models (LLMs) can react to feedback from interface invocations in …
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An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
2018 · arXiv (Cornell University)
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new …
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Deep Equilibrium Models
2019 · arXiv (Cornell University)
We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose …