From ed4bfd95f0f25dd67d7b2ab31c36200a9ee5c421 Mon Sep 17 00:00:00 2001 From: blrain Date: Thu, 17 Sep 2026 08:40:25 +0800 Subject: [PATCH] docs(ml): add Mamba paper to machine_learning index (#893) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" by Albert Gu and Tri Dao (2023) — a state-space model with linear-time sequence processing that rivals Transformer performance while enabling extremely long context windows with lower compute. --- machine_learning/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/machine_learning/README.md b/machine_learning/README.md index 550667c..cd66939 100644 --- a/machine_learning/README.md +++ b/machine_learning/README.md @@ -42,6 +42,10 @@ This paper introduces AlexNet, a neural network architecture which dramatically improved over the state-of-the-art in image classification algorithms and is widely regarded as a breakthrough moment for deep learning. +* [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/pdf/2312.00752.pdf) by Albert Gu and Tri Dao + + This paper introduces Mamba, a state-space model with linear-time sequence processing that rivals Transformer performance while enabling extremely long context windows with lower compute — challenging the dominance of attention-based architectures for sequence modeling. + * [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf) This paper introduces the foundations of the rapidly emerging field of interpretable machine learning.