• LSTM

  • Referenced in 30 articles [sw03373]
  • explains the rapidly growing interest in artificial RNNs for technical applications: general computers which...
  • Evolino

  • Referenced in 19 articles [sw36450]
  • Most supervised gradient-based recurrent neural networks (RNNs) suffer from a vanishing error signal that...
  • CNN-RNN

  • Referenced in 8 articles [sw28401]
  • this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs...
  • Zoneout

  • Referenced in 5 articles [sw36444]
  • Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. We propose zoneout, a novel method ... regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous...
  • Transformer-XL

  • Referenced in 5 articles [sw36208]
  • learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves...
  • Clockwork RNN

  • Referenced in 5 articles [sw36448]
  • between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope...
  • TopicRNN

  • Referenced in 3 articles [sw36211]
  • latent topics. Because of their sequential nature, RNNs are good at capturing the local structure ... proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local...
  • Conformer

  • Referenced in 4 articles [sw35794]
  • Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content...
  • CURRENNT

  • Referenced in 1 article [sw12814]
  • parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA ... Architecture (CUDA). CURRENNT supports uni- and bidirectional RNNs with Long Short-Term Memory (LSTM) memory ... publicly available parallel implementation of deep LSTM-RNNs. Benchmarks are given on a noisy speech ... Speech Separation and Recognition Challenge, where LSTM-RNNs have been shown to deliver best performance...
  • FastGRNN

  • Referenced in 1 article [sw40550]
  • resource-constrained and real-time applications. Unitary RNNs have increased accuracy somewhat by restricting ... loss in expressive power. Gated RNNs have obtained state-of-the-art accuracies by adding ... smaller than leading gated and unitary RNNs. This allowed FastGRNN to accurately recognize...
  • Skip RNN

  • Referenced in 1 article [sw36445]
  • Recurrent Neural Networks. Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling ... tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients...
  • DeepStellar

  • Referenced in 1 article [sw41843]
  • criteria which enable the quantitative analysis of RNNs. We further propose two algorithms powered ... useful in capturing the internal behaviors of RNNs, and confirm that (1) the similarity metrics...
  • CrySSMEx

  • Referenced in 1 article [sw01960]
  • Neural Networks. Input: sequential data generated from RNNs. Output: (stochastic) FSMs and state space quantizers...
  • TPA-LSTM

  • Referenced in 1 article [sw34694]
  • achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews...
  • Shiftry

  • Referenced in 1 article [sw40549]
  • provide first empirical evaluation of RNNs running on tiny edge devices. On simpler ML models...
  • AUTOTRAINER

  • Referenced in 1 article [sw41850]
  • CNNs) for image and Recurrent Neural Networks (RNNs) for texts. Our evaluation on 6 datasets...
  • CVX

  • Referenced in 832 articles [sw04594]
  • CVX is a modeling system for constructing and...
  • Theano

  • Referenced in 95 articles [sw05894]
  • Theano is a Python library that allows you...
  • LMI toolbox

  • Referenced in 1463 articles [sw06383]
  • Linear Matrix Inequalities (LMIs) and LMI techniques have...