• PointRNN

  • Referenced in 1 article [sw36644]
  • PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing. In this paper, we introduce ... Point Recurrent Neural Network (PointRNN) for moving point cloud processing. At each time step, PointRNN ... variants of PointRNN, i.e., Point Gated Recurrent Unit (PointGRU) and Point Long Short-Term Memory...
  • CURRENNT

  • Referenced in 1 article [sw12814]
  • CURRENNT: the Munich open-source CUDA recurrent neural network toolkit. In this article, we introduce ... open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs...
  • hebbRNN

  • Referenced in 1 article [sw29194]
  • Reward-Modulated Hebbian Learning Rule for Recurrent Neural Networks. How does our brain learn ... motor skills using neural networks. However, training these networks to produce meaningful behavior has proven ... biologically-plausible training rule for recurrent neural networks using a delayed and sparse reward signal...
  • char-rnn

  • Referenced in 1 article [sw27212]
  • This code implements multi-layer Recurrent Neural Network (RNN, LSTM, and GRU) for training/sampling from ... file as input and trains a Recurrent Neural Network that learns to predict the next...
  • Bio-LSTM

  • Referenced in 1 article [sw25897]
  • LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction ... This paper proposes a biomechanically inspired recurrent neural network (Bio-LSTM) that can predict...
  • Leabra7

  • Referenced in 1 article [sw26111]
  • Python package for modeling recurrent, biologically-realistic neural networks. Emergent is a software package that ... algorithm to simulate and train arbitrary recurrent neural network architectures in a biologically-realistic manner ... with Python’s scientific stack. We demonstrate recurrent Leabra7 networks using traditional pattern-association tasks...
  • ConvS2S

  • Referenced in 1 article [sw26536]
  • variable length output sequence via recurrent neural networks. We introduce an architecture based entirely ... convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized...
  • PoPMnet

  • Referenced in 1 article [sw36063]
  • Structure Generation Net (SGN) and a Recurrent Neural Network (RNN)-based Melody Generation...
  • auDeep

  • Referenced in 1 article [sw27628]
  • representations from audio with deep recurrent neural networks. auDeep is a Python toolkit for deep ... acoustic data. It is based on a recurrent sequence to sequence autoencoder approach which...
  • CrySSMEx

  • Referenced in 1 article [sw01960]
  • extracting Finite State Machines from Recurrent Neural Networks. Input: sequential data generated from RNNs. Output...
  • Synaptic

  • Referenced in 1 article [sw27138]
  • first order or even second order neural network architectures. This library includes a few built ... short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable ... training any given network, which includes built-in training tasks/tests like solving an XOR, completing ... like training algorithm for second-order recurrent neural networks...
  • Ithemal

  • Referenced in 1 article [sw25886]
  • Basic Block Throughput Estimation using Deep Neural Networks. Statically estimating the number of processor clock ... uses a novel Directed Acyclic Graph-Recurrent Neural Network (DAG-RNN) based data-driven approach...
  • ARGUS framework

  • Referenced in 1 article [sw15168]
  • system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification...
  • SignalP

  • Referenced in 1 article [sw27722]
  • based on a deep convolutional and recurrent neural network architecture including a conditional random field...
  • OSTSC

  • Referenced in 1 article [sw22352]
  • /TKDE.2013.37> and adapted for use with Recurrent Neural Networks implemented in ’TensorFlow...
  • rna-state-inf

  • Referenced in 1 article [sw33162]
  • prediction via state inference with deep recurrent neural networks. The problem of determining which nucleotides...
  • TPA-LSTM

  • Referenced in 1 article [sw34694]
  • data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism...
  • DeLS-3D

  • Referenced in 1 article [sw36651]
  • incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve...
  • DeepAffinity

  • Referenced in 1 article [sw39160]
  • Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks. Motivation: Drug discovery demands rapid ... deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit...
  • keras

  • Referenced in 10 articles [sw22353]
  • Keras’ , a high-level neural networks ’API’. ’Keras’ was developed with a focus ... fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations...