Alternatives and similar repositories for scalingDTP

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

• meulemansalex / theoretical_framework_for_target_propagation
  Python implementation of the methods in Meulemans et al. 2020 - A Theoretical Framework For Target Propagation
  ☆32Updated 7 months ago
• ernoult / targetProp
  Testing Difference Target Propagation (DTP) on MNIST.
  ☆12Updated 4 years ago
• EPFL-LCN / pub-illing2021-neurips
  Public code for Illing, Ventura, Bellec & Gerstner 2021: Local plasticity rules can learn deep representations using self-supervised cont…
  ☆24Updated last year
• fKunstner / noise-sgd-adam-sign
  ☆16Updated 2 years ago
• locuslab / orthogonal-convolutions
  Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness
  ☆45Updated 4 years ago
• VITA-Group / Lifelong-Learning-LTH
  [ICLR 2021] "Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, S…
  ☆25Updated 3 years ago
• pkuxmq / IDE-FSNN
  [NeurIPS 2021] Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
  ☆29Updated 3 years ago
• romanpogodin / towards-bio-plausible-conv
  ☆14Updated 3 years ago
• ernoult / updatesEPgradientsBPTT
  Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input (NeurIPS 2019)
  ☆14Updated last year
• stephane-rivaud / ForwardLocalGradient
  This is the official implementation of the ICML 2023 paper - Can Forward Gradient Match Backpropagation ?
  ☆12Updated 2 years ago
• ThomasMiconi / HebbianCNNPyTorch
  Automatic Hebbian learning in multi-layer convolutional networks with PyTorch, by expressing Hebbian plasticity rules as gradients
  ☆38Updated 2 years ago
• AndreevP / FRCL
  Functional Regularisation for Continual Learning with Gaussian Processes
  ☆14Updated 4 years ago
• ml-research / self-expanding-neural-networks
  Self-Expanding Neural Networks
  ☆39Updated last year
• briancheung / superposition
  ☆45Updated 5 years ago
• naver / force
  ☆14Updated 4 years ago
• smonsays / contrastive-meta-learning
  Code accompanying the paper "A contrastive rule for meta-learning"
  ☆12Updated 7 months ago
• NeuromorphicComputing / STPN
  ☆29Updated last year
• a554b554 / kWTA-Activation
  ☆47Updated 5 years ago
• NicolasZucchet / Online-learning-LR-dependencies
  Implementation of the "Online learning of long-range dependencies" paper, NeurIPS 2023
  ☆18Updated 7 months ago
• Shiweiliuiiiiiii / Sparse_Topology_Distance
  [ECMLPKDD 2020] "Topological Insights into Sparse Neural Networks"
  ☆13Updated 3 years ago
• shwinshaker / LipGrow
  An adaptive training algorithm for residual network
  ☆15Updated 4 years ago
• zhaoyang-0204 / gnp
  gradient norm penalty
  ☆40Updated last year
• mil-ad / prospr
  Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients
  ☆31Updated 3 years ago
• mariacer / strong_dfc
  Minimizing Control for Credit Assignment with Strong Feedback
  ☆14Updated 7 months ago
• L0SG / feedback-alignment-pytorch
  Implementation of feedback alignment learning in PyTorch
  ☆31Updated 2 years ago
• AhmedImtiazPrio / grok-adversarial
  Deep Networks Grok All the Time and Here is Why
  ☆37Updated last year
• isapome / BrainProp
  BrainProp: How the brain can implement reward-based error backpropagation
  ☆16Updated 2 years ago
• singlasahil14 / SOC
  Code for the ICML 2021 and ICLR 2022 papers: Skew Orthogonal Convolutions, Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
  ☆18Updated 3 years ago
• Intelligent-Computing-Lab-Yale / Exploring-Lottery-Ticket-Hypothesis-in-SNNs
  Exploring Lottery Ticket Hypothesis in Sparse Spiking Neural Networks (ECCV2022, oral presentation)
  ☆34Updated 2 years ago
• NeuromorphicComputing / SoftHebb
  ☆79Updated last year