memorizing-transformers-pytorch
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
Implement and experiment with advanced memory techniques in AI models using PyTorch.
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Free · no card · unsubscribe anytimeImplementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
memorizing-transformers-pytorch has 646 stars on GitHub. It has been forked 48 times. memorizing-transformers-pytorch is written mainly in Python. It has been in active development since 2022. memorizing-transformers-pytorch is available under the MIT license. Its main topics are approximate-nearest-neighbors, artificial-intelligence, attention-mechanism, deep-learning.
What is memorizing-transformers-pytorch?
Implementation of Memorizing Transformers (ICLR 2022), attention net augmented with indexing and retrieval of memories using approximate nearest neighbors, in Pytorch
Is memorizing-transformers-pytorch open source?
memorizing-transformers-pytorch is an open-source project. It is released under the MIT license.
Is memorizing-transformers-pytorch free?
Yes. memorizing-transformers-pytorch is free and open source — you can use, modify and self-host it.
What license does memorizing-transformers-pytorch use?
memorizing-transformers-pytorch is available under the MIT license.
What language is memorizing-transformers-pytorch written in?
memorizing-transformers-pytorch is written mainly in Python.
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