Alternatives and similar repositories for random-fourier-features-pytorch

Users that are interested in random-fourier-features-pytorch are comparing it to the libraries listed below

• ndahlquist / pytorch-fourier-feature-networks
  Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
  ☆49Updated 5 years ago
• samuelepapa / fit-a-nef
  🚀 A powerful library for efficient training of Neural Fields at scale.
  ☆29Updated last year
• google-deepmind / functa
  ☆158Updated last year
• boschresearch / multiplicative-filter-networks
  Source code for Fathony, Sahu, Willmott, & Kolter, "Multiplicative Filter Networks", ICLR 2021.
  ☆95Updated 4 years ago
• fwilliams / scalable-pytorch-sinkhorn
  ☆156Updated 2 years ago
• samuelepapa / neural-field-arena
  ☆12Updated last year
• danielegrattarola / GINR
  Code for "Generalised Implicit Neural Representations" (NeurIPS 2022).
  ☆71Updated 2 years ago
• vishwa91 / wire
  wavelet implicit neural representations
  ☆165Updated last year
• locuslab / torchdeq
  Modern Fixed Point Systems using Pytorch
  ☆102Updated last year
• LituRout / OptimalTransportModeling
  Generative Modeling with Optimal Transport Maps - ICLR 2022
  ☆60Updated 3 years ago
• AaltoML / generative-inverse-heat-dissipation
  Code release for the paper Generative Modeling With Inverse Heat Dissipation
  ☆74Updated 2 years ago
• AlexGraikos / diffusion_priors
  Using pre-trained Diffusion models as priors for inference tasks
  ☆209Updated 2 years ago
• qsh-zh / gDDIM
  [ICLR'23 Spotlight] gDDIM: analyze and accelerate general diffusion models, isotropic and non-isotropic
  ☆49Updated last year
• FrederikWarburg / LaplaceAE
  ☆32Updated 2 years ago
• jw9730 / setvae
  [CVPR'21] SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data, in PyTorch
  ☆75Updated 2 years ago
• facebookresearch / generalized-schrodinger-bridge-matching
  code for "Generalized Schrödinger Bridge Matching" (ICLR 2024).
  ☆73Updated last year
• vislearn / FFF
  Free-form flows are a generative model training a pair of neural networks via maximum likelihood
  ☆47Updated last month
• EmilienDupont / neural-function-distributions
  Pytorch implementation of Generative Models as Distributions of Functions 🌿
  ☆150Updated last year
• gortizji / inr_dictionaries
  Source code of "A structured dictionary perspective on implicit neural representations"
  ☆60Updated 3 years ago
• locuslab / ImpSq
  Implicit^2: Implicit model for implicit neural representations
  ☆29Updated 3 years ago
• xqding / RealNVP
  A PyTorch Implementation of Density Estimation Using Real NVP
  ☆74Updated 4 years ago
• KingJamesSong / latent-flow
  NeurIPS23 "Flow Factorized Representation Learning"
  ☆37Updated 9 months ago
• VITA-Group / INSP
  [NeurIPS 2022] "Signal Processing for Implicit Neural Representations" by Dejia Xu*, Peihao Wang*, Yifan Jiang, Zhiwen Fan, Zhangyang Wan…
  ☆52Updated 2 years ago
• EmilienDupont / coinpp
  Pytorch implementation of COIN++ 🍁
  ☆73Updated 3 years ago
• yinboc / trans-inr
  Transformers as Meta-Learners for Implicit Neural Representations, in ECCV 2022
  ☆154Updated 2 years ago
• nasimrahaman / SpectralBias
  Code for "On the Spectral Bias of Neural Networks", to appear in ICML 2019 (Long Beach, CA).
  ☆108Updated 6 years ago
• ELIFE-ASU / INNLab
  A python/pytorch package for invertible neural networks
  ☆65Updated 2 years ago
• Typal-Research / jacobian_free_backprop
  Implicit networks can be trained efficiently and simply by using Jacobian-free Backprop (JFB).
  ☆38Updated 3 years ago
• psh01087 / Vid-ODE
  Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
  ☆63Updated 2 years ago
• Rajhans0 / Poly_INR
  Code for our CVPR'23 paper: "Polynomial Implicit Neural Representations For Large Diverse Datasets"
  ☆36Updated 2 years ago