arXiv:2410.11687v3 Announce Type: replace-cross Abstract: Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient descent. We propose a sufficient constructive inductive bias for LRNNs: equip a diagonal recurrent state with multiplicative readout and a short sliding-window cross-product self-attention update. The resulting architecture, Gradient-based Recurrent In-context Learner (GRIL), can implement minibatch gradient descent on a task-specific linear predictor during a
The continuous drive for more efficient and interpretable AI learning architectures, especially for sequential data, is leading researchers to explore novel recurrent network designs.
This development could lead to more robust and resource-efficient AI models capable of in-context learning, impacting the development and deployment of advanced AI systems.
The proposed GRIL architecture offers a potential pathway to implement gradient descent more directly within recurrent networks, enhancing their learning capabilities without relying on traditional backpropagation through time.
- · AI researchers
- · Developers of sequential data models
- · Edge AI computing
- · Autonomous systems
- · AI models reliant on extensive backpropagation through time
Improved efficiency and interpretability of recurrent neural networks for tasks like generative AI and real-time processing.
Reduced computational overhead for training certain types of AI models, lowering barriers to entry for smaller research groups and developers.
Accelerated development of AI agents that can rapidly adapt and learn from new data in real-world, dynamic environments.
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Read at arXiv cs.AI