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A minimalist MVP demonstrating a simple yet profound insight: aligning AI memory with human episodic memory granularity. Shows how this single principle enables simple methods to rival complex memory frameworks for conversational tasks.
A Python(PyTorch) implementation of memory augmented neural network based on Ritter et al. (2018). Been There, Done That: Meta-Learning with Episodic Recall. ICML.
PyTorch implementation of Episodic Meta Reinforcement Learning on variants of the "Two-Step" task. Reproduces the results found in three papers. Check the ReadMe for more details!
This project explores the integration of an episodic memory module into PyACTR. The goal is to enhance the agent's performance in language processing tasks that require context and pattern recognition.
Official implementation of the paper "Linking In-context Learning in Transformers to Human Episodic Memory" by Li Ji-An, Corey Zhou, Marcus Benna, and Marcelo Mattar