HMT: Hierarchical Memory Transformer for Long Context Language Processing

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  • HMT-pytorch

    Official Implementation of "HMT: Hierarchical Memory Transformer for Long Context Language Processing"

  • Code: https://github.com/OswaldHe/HMT-pytorch

    This looks really interesting. I've the paper to my reading list and look forward to playing with the code. I'm curious to see what kinds of improvements we can get by agumenting Transformers and other generative language/sequence models with this and other mechanisms implementing hierarchical memory.[a]

    We sure live in interesting times!

    ---

    [a] In the past, I experimented a little with transformers that had access to external memory using https://github.com/lucidrains/memorizing-transformers-pytorc... and also using routed queries with https://github.com/glassroom/heinsen_routing . Both approaches seemed to work, but I never attempted to build any kind of hierarchy with those approaches.

  • memorizing-transformers-pytorch

    Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch

  • Code: https://github.com/OswaldHe/HMT-pytorch

    This looks really interesting. I've the paper to my reading list and look forward to playing with the code. I'm curious to see what kinds of improvements we can get by agumenting Transformers and other generative language/sequence models with this and other mechanisms implementing hierarchical memory.[a]

    We sure live in interesting times!

    ---

    [a] In the past, I experimented a little with transformers that had access to external memory using https://github.com/lucidrains/memorizing-transformers-pytorc... and also using routed queries with https://github.com/glassroom/heinsen_routing . Both approaches seemed to work, but I never attempted to build any kind of hierarchy with those approaches.

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

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  • Code: https://github.com/OswaldHe/HMT-pytorch

    This looks really interesting. I've the paper to my reading list and look forward to playing with the code. I'm curious to see what kinds of improvements we can get by agumenting Transformers and other generative language/sequence models with this and other mechanisms implementing hierarchical memory.[a]

    We sure live in interesting times!

    ---

    [a] In the past, I experimented a little with transformers that had access to external memory using https://github.com/lucidrains/memorizing-transformers-pytorc... and also using routed queries with https://github.com/glassroom/heinsen_routing . Both approaches seemed to work, but I never attempted to build any kind of hierarchy with those approaches.

  • heinsen_routing

    Reference implementation of "An Algorithm for Routing Vectors in Sequences" (Heinsen, 2022) and "An Algorithm for Routing Capsules in All Domains" (Heinsen, 2019), for composing deep neural networks.

  • Code: https://github.com/OswaldHe/HMT-pytorch

    This looks really interesting. I've the paper to my reading list and look forward to playing with the code. I'm curious to see what kinds of improvements we can get by agumenting Transformers and other generative language/sequence models with this and other mechanisms implementing hierarchical memory.[a]

    We sure live in interesting times!

    ---

    [a] In the past, I experimented a little with transformers that had access to external memory using https://github.com/lucidrains/memorizing-transformers-pytorc... and also using routed queries with https://github.com/glassroom/heinsen_routing . Both approaches seemed to work, but I never attempted to build any kind of hierarchy with those approaches.

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