Alternatives and similar repositories for SparseBayesianRNN:

Users that are interested in SparseBayesianRNN are comparing it to the libraries listed below

• wenwei202 / iss-rnns
  Sparse Recurrent Neural Networks -- Pruning Connections and Hidden Sizes (TensorFlow)
  ☆74Updated 4 years ago
• houlu369 / Loss-aware-weight-quantization
  Implementation of ICLR 2018 paper "Loss-aware Weight Quantization of Deep Networks"
  ☆26Updated 5 years ago
• houlu369 / Loss-aware-Binarization
  Implementation of ICLR 2017 paper "Loss-aware Binarization of Deep Networks"
  ☆18Updated 5 years ago
• stevenygd / SWALP
  Code for paper "SWALP: Stochastic Weight Averaging forLow-Precision Training".
  ☆62Updated 5 years ago
• gaosh / Structured-Bayesian-Pruning-pytorch
  pytorch implementation of Structured Bayesian Pruning
  ☆19Updated 6 years ago
• TomVeniat / SANAS
  Stochastic Adaptive Neural Architecture Search
  ☆65Updated 6 years ago
• csyhhu / Co-Prune
  Codes for accepted paper "Cooperative Pruning in Cross-Domain Deep Neural Network Compression" in IJCAI 2019.
  ☆11Updated 5 years ago
• Andrew-Tierno / QuantizedTransformer
  Implementation of a Quantized Transformer Model
  ☆18Updated 5 years ago
• csyhhu / L-DNQ
  Codes for AAAI2019 paper: Deep Neural Network Quantization via Layer-Wise Optimization using Limited Training Data
  ☆41Updated 6 years ago
• khakhulin / compressed-transformer
  Compression of NMT transformer model with tensor methods
  ☆48Updated 5 years ago
• tajanthan / pmf
  Proximal Mean-field for Neural Network Quantization
  ☆22Updated 4 years ago
• TropComplique / trained-ternary-quantization
  Reducing the size of convolutional neural networks
  ☆114Updated 7 years ago
• demelin / Noise-Contrastive-Estimation-NCE-for-pyTorch
  Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation pr…
  ☆45Updated 5 years ago
• arkmagus / tensor_rnn
  An implementation of various tensor-based decomposition for NN & RNN parameters
  ☆20Updated 6 years ago
• yuhuixu1993 / WLQ
  caffe implementation of single level quantization
  ☆19Updated 6 years ago
• houlu369 / Normalized-Quantized-LSTM
  Implementation of NeurIPS 2019 paper "Normalization Helps Training of Quantized LSTM"
  ☆30Updated 6 months ago
• KarenUllrich / Tutorial_BayesianCompressionForDL
  A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).
  ☆205Updated 6 years ago
• alexfjw / prunnable-layers-pytorch
  Prunable nn layers for pytorch.
  ☆48Updated 6 years ago
• necludov / group-sparsity-sbp
  Structured Bayesian Pruning, NIPS 2017
  ☆73Updated 4 years ago
• Separius / CudaRelativeAttention
  custom cuda kernel for {2, 3}d relative attention with pytorch wrapper
  ☆43Updated 4 years ago
• JiJingYu / delta_orthogonal_init_pytorch
  Delta Orthogonal Initialization for PyTorch
  ☆18Updated 6 years ago
• zhuohan123 / g2-lstm
  Codes for "Towards Binary-Valued Gates for Robust LSTM Training".
  ☆76Updated 6 years ago
• McDonnell-Lab / 1-bit-per-weight
  Training wide residual networks for deployment using a single bit for each weight
  ☆36Updated 4 years ago
• qinyao-he / bit-rnn
  Quantize weights and activations in Recurrent Neural Networks.
  ☆94Updated 6 years ago
• BayesWatch / deficient-efficient
  Successfully training approximations to full-rank matrices for efficiency in deep learning.
  ☆16Updated 4 years ago
• hehaodele / ProbGAN
  [ICLR 2019] ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
  ☆32Updated 5 years ago
• BayesWatch / pytorch-prunes
  Code for https://arxiv.org/abs/1810.04622
  ☆140Updated 5 years ago
• team-approx-bayes / BayesBiNN
  Code for the paper "Training Binary Neural Networks with Bayesian Learning Rule
  ☆37Updated 3 years ago
• uclaml / Padam
  Partially Adaptive Momentum Estimation method in the paper "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep …
  ☆39Updated last year
• ruihangdu / PyTorch-Deep-Compression
  A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)
  ☆40Updated 6 years ago