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Chrono Gate Initialization Boosts LSTM/GRU Performance on Long Dependencies

By PulseAugur Editorial · [3 sources] ·

A new study published on arXiv investigates the effectiveness of gate initialization techniques versus recurrent dropout and zoneout in Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks for learning long dependencies. The research trained networks on synthetic tasks, finding that chrono gate initialization significantly improved performance, enabling all runs to reach validation criteria within 5000 updates. In contrast, conventional initialization struggled, with dropout generally delaying learning and zoneout offering limited benefits under specific conditions. AI

IMPACT This research offers insights into optimizing recurrent neural networks for tasks requiring long-term memory, potentially improving performance in sequence modeling applications.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a controlled study of neural network architectures and training techniques.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Chrono Gate Initialization Boosts LSTM/GRU Performance on Long Dependencies

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Oleksandr Kholodniak, Oleksandr Prokhorov ·

    Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks

    arXiv:2610.29034v1 Announce Type: new Abstract: Dropout and zoneout are routinely added to long short-term memory (LSTM) and gated recurrent unit (GRU) networks, but, to our knowledge, their placements have not been crossed with gate initialisation in a factorial design, and it i…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Oleksandr Prokhorov ·

    Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks

    Dropout and zoneout are routinely added to long short-term memory (LSTM) and gated recurrent unit (GRU) networks, but, to our knowledge, their placements have not been crossed with gate initialisation in a factorial design, and it is not known whether early gradient propagation t…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks

    Dropout and zoneout are routinely added to long short-term memory (LSTM) and gated recurrent unit (GRU) networks, but, to our knowledge, their placements have not been crossed with gate initialisation in a factorial design, and it is not known whether early gradient propagation t…

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