Methods › General › Deep Tabular Learning › ODL
online deep learning
ODL
Introduced by Doyen Sahoo et al. in Online Deep Learning: Learning Deep Neural Networks on the Fly
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings.
Papers archive 2025-07-28
11 shown of 11, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms 21 Mar 2025 · 0 repositories · arXiv:2503.17436
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Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces 13 Sep 2024 · 1 repository · arXiv:2409.09161
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A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition 2 Aug 2024 · 0 repositories · arXiv:2408.01283
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MODL: Multilearner Online Deep Learning 28 May 2024 · 1 repository · arXiv:2405.18281
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Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers 30 May 2023 · 1 repository · arXiv:2305.19167
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Hierarchical Optimization-Derived Learning 11 Feb 2023 · 0 repositories · arXiv:2302.05587
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EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems 26 Sep 2022 · 2 repositories · arXiv:2209.12766
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Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training 16 Jun 2022 · 0 repositories · arXiv:2206.07875
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Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning 5 May 2020 · 0 repositories · arXiv:2005.02356
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A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning 1 Dec 2019 · 0 repositories
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Online Deep Learning: Learning Deep Neural Networks on the Fly 10 Nov 2017 · 5 repositories · arXiv:1711.03705
Tasks archive 2025-07-28
15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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