arXiv Machine Learning By Akarsh Kumar, Phillip Isola

Pretraining Recurrent Networks without Recurrence

Read the original on arXiv Machine Learning →

arXiv:2606. 06479v1 Announce Type: new Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations.

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arXiv AI
Jun 4

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

arXiv:2506. 05233v2 Announce Type: replace-cross Abstract: Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention.

By Johannes von Oswald, Nino Scherrer, Seijin Kobayashi, Luca Versari, Songlin Yang, Sarthak Mittal, Maximilian Schlegel, Kaitlin Maile, Yanick Schimpf, Oliver Sieberling, Alexander Meulemans, Rif A. Saurous, Guillaume Lajoie, Charlotte Frenkel, Razvan Pascanu, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento
arXiv Machine Learning
Aug 5

Maglev: Sliding Recurrent Memory

arXiv:2608. 02870v1 Announce Type: new Abstract: We introduce \ours{}, a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training.

By Bo Liu, Qiang Liu
arXiv AI
Jun 9

MinMax Recurrent Neural Cascades

arXiv:2605. 06384v3 Announce Type: replace-cross Abstract: We introduce MinMax Recurrent Neural Cascades (MinMax RNCs), a class of recurrent neural networks built from a novel form of recurrence over the MinMax algebra.

By Alessandro Ronca