Unofficial implementation of Titans, SOTA memory for transformers, in Pytorch

PyTorch implementation of Titans.

A proof-of-concept implementation of Titans: models mixing long-term, short-term and persistent memories

PyTorch implementation of Google Titans: Learning to Memorize at Test Time (arXiv:2501.00663)

Unofficial Implementation of Titans: Learning to Memorize at Test Time

Segmentation of skin cancer images for submission to ISIC 2018 using deep learning. Base built on RECOD Titans' implementation.

Unofficial implementation of titans transformer

A Java implementation of 2-Player Chess, based on Windows' Chess Titans.

This repository contains an experimental implementation of the Titans Transformer architecture for sequence modeling tasks. The code is a personal exploration and may include errors or inefficiencies as I am currently in the learning stage. It is inspired by the ideas presented in the original

Titans paper implementation

Simple implementation of paper - Titans: Learning to Memorize at Test Time

Titans: Learning to Memorize at Test Time — PyTorch implementation with TPTT support

A simple adapter implementation to transform pretrained Transformer-family models into the Titans architecture.

A PyTorch implementation inspired by the public domain paper Titans: Learning to Memorize at Test Time

A vector database implementation inspired by the "Titans: Learning to Memorize at Test Time" paper that manages hierarchical memory systems for handling extremely long contexts.

From-scratch PyTorch implementation of Google's Titans MAC architecture. Real Test-Time Training — LMM updates its own weights during inference. 6-layer DeepMAC, ~200M parameters. No pretrained models.

Exploration into TITANs architecture implementation

Implementation of the Titans model for long-term memory in language models

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Titans: Learning to Memorize at Test Time arXiv:2501.00663 [cs.LG]

Unofficial implementation of Titans

A simple implementation of TITANs Paper by Google

Implementation of Titans: Learning to memorise at test time

An Implementation Of The Titans Architecture Built off of R1

Unofficial Implementation of Titans - Learning to Memorize at Test Time from Google Research.

PyTorch implementation of Self-Modifying Titans and Continuum Memory System from the Nested Learning paper

High-performance CUDA implementation of Titans neural memory architecture (Learning to Memorize at Test Time)

This is a proof-of-concept implementation for VisionTitans, exploring the application of Titans' neural memory to vision architectures.

Official PyTorch implementation of Mixture-of-Experts KAN (MoE-KAN) for medical reasoning. Benchmarks KAN vs. MLP, analyzing parameter efficiency, spline interpretability, and noise robustness on the Titans Medical Dataset.