Alternatives and similar repositories for miracle

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

• amirgholami / HessianFlow
  ☆83Updated 5 years ago
• BayesWatch / deficient-efficient
  Successfully training approximations to full-rank matrices for efficiency in deep learning.
  ☆17Updated 4 years ago
• jornpeters / integer_discrete_flows
  Code release for Hoogeboom, Emiel, Jorn WT Peters, Rianne van den Berg, and Max Welling. "Integer Discrete Flows and Lossless Compression…
  ☆100Updated 5 years ago
• fmi-basel / neural-tangent-transfer
  Code accompanying our paper "Finding trainable sparse networks through Neural Tangent Transfer" to be published at ICML-2020.
  ☆13Updated 5 years ago
• mingzhang-yin / ARM-gradient
  Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)
  ☆27Updated 6 years ago
• modestyachts / neural_kernels_code
  ☆36Updated 4 years ago
• toiaydcdyywlhzvlob / backpack
  This repository is no longer maintained. Check
  ☆81Updated 5 years ago
• gd-zhang / Weight-Decay
  Regularization, Neural Network Training Dynamics
  ☆14Updated 5 years ago
• tajanthan / pmf
  Proximal Mean-field for Neural Network Quantization
  ☆21Updated 5 years ago
• team-approx-bayes / BayesBiNN
  Code for the paper "Training Binary Neural Networks with Bayesian Learning Rule
  ☆39Updated 3 years ago
• yanivbl6 / quantized_meanfield
  This repository provides code source used in the paper: A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
  ☆13Updated 6 years ago
• ColinQiyangLi / LConvNet
  Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)
  ☆35Updated 5 years ago
• wouterkool / estimating-gradients-without-replacement
  Estimating Gradients for Discrete Random Variables by Sampling without Replacement
  ☆40Updated 5 years ago
• mitscha / strassennets
  Deep learning with a multiplication budget
  ☆47Updated 7 years ago
• fdraxler / PyTorch-AutoNEB
  PyTorch AutoNEB implementation to identify minimum energy paths, e.g. in neural network loss landscapes
  ☆56Updated 3 years ago
• wendazhou / nnet-compression-generalization
  ☆27Updated 6 years ago
• fKunstner / limitations-empirical-fisher
  Limitations of the Empirical Fisher Approximation
  ☆48Updated 7 months ago
• ganguli-lab / deepchaos
  Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"
  ☆73Updated 9 years ago
• yanghr / DeepHoyer
  DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
  ☆32Updated 5 years ago
• alinlab / lookahead_pruning
  Lookahead: A Far-sighted Alternative of Magnitude-based Pruning (ICLR 2020)
  ☆32Updated 4 years ago
• afriesen / ftprop
  Feasible target propagation code for the paper "Deep Learning as a Mixed Convex-Combinatorial Optimization Problem" by Friesen & Domingos…
  ☆28Updated 7 years ago
• KarenUllrich / Tutorial_BayesianCompressionForDL
  A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).
  ☆206Updated 6 years ago
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆54Updated 6 years ago
• ehoogeboom / emerging
  ☆42Updated 6 years ago
• stevenygd / SWALP
  Code for paper "SWALP: Stochastic Weight Averaging forLow-Precision Training".
  ☆62Updated 6 years ago
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 8 years ago
• xuexue / randombp
  Demo: Slightly More Bio-Plausible Backprop
  ☆21Updated 8 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• AlliedToasters / synapses
  ☆71Updated 5 years ago
• JiJingYu / delta_orthogonal_init_pytorch
  Delta Orthogonal Initialization for PyTorch
  ☆18Updated 7 years ago