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New method trains recurrent networks without recurrence

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called Supervised Memory Training (SMT) to pretrain recurrent neural networks (RNNs) without relying on traditional recurrence. SMT trains RNNs by reducing the process to supervised learning on memory transitions, bypassing the limitations of backpropagation through time. This approach enables time-parallel training and a stable gradient path, potentially allowing for more scalable RNN architectures capable of capturing long-range dependencies. AI

IMPACT Enables parallel training of RNNs, potentially unlocking scaling for models that build temporal abstractions.

RANK_REASON The cluster contains an academic paper detailing a new method for training neural networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method trains recurrent networks without recurrence

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The cluster contains an academic paper detailing a new method for training neural networks.
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paper, model release
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Akarsh Kumar, Phillip Isola ·

    Pretraining Recurrent Networks without Recurrence

    arXiv:2606.06479v1 Announce Type: new Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, a…

  2. arXiv cs.AI TIER_1 English(EN) · Phillip Isola ·

    Pretraining Recurrent Networks without Recurrence

    Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, and suffers from vanishing or exploding gradients…

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