Alternatives and similar repositories for 2023-transformers:

Users that are interested in 2023-transformers are comparing it to the libraries listed below

• juliagusak / neural-ode-metasolver
  Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561
  ☆24Updated 3 years ago
• thudzj / ELLA
  Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')
  ☆16Updated 2 years ago
• jmtomczak / git_flow
  General Invertible Transformations for Flow-based Generative Models
  ☆17Updated 4 years ago
• brandstetter-johannes / LPSDA
  Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"
  ☆49Updated last year
• locuslab / monotone_op_net
  Monotone operator equilibrium networks
  ☆51Updated 4 years ago
• gebob19 / introduction_to_normalizing_flows
  Jupyter Notebook corresponding to 'Going with the Flow: An Introduction to Normalizing Flows'
  ☆25Updated 3 years ago
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆44Updated last year
• michaelsdr / sinkformers
  Transformers with doubly stochastic attention
  ☆44Updated 2 years ago
• ryoungj / mcbits
  [ICML'21 Oral] Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
  ☆14Updated 3 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆21Updated 4 years ago
• facebookresearch / neural-conservation-law
  code for "Neural Conservation Laws A Divergence-Free Perspective".
  ☆35Updated 2 years ago
• ml-jku / quam
  Quantification of Uncertainty with Adversarial Models
  ☆27Updated last year
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆39Updated 3 years ago
• LCS2-IIITD / TransEvolve
  ☆11Updated 3 years ago
• DiffEqML / kairos
  Model hub for all your DiffeqML needs. Pretrained weights, modules, and basic inference infrastructure
  ☆24Updated last year
• Tom-Ryder / VIforSDEs
  A variational method for fast, approximate inference for stochastic differential equations.
  ☆43Updated 6 years ago
• yizhangzzz / transformers-lego
  ☆18Updated 2 years ago
• takerum / meta_sequential_prediction
  ☆18Updated last year
• ermongroup / SPN_Variational_Inference
  PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
  ☆15Updated 3 years ago
• srush / mamba-scans
  Blog post
  ☆16Updated 11 months ago
• devvrit / SONew
  ☆9Updated last year
• remilepriol / causal-adaptation-speed
  Investigate the speed of adaptation of structural causal models
  ☆16Updated 3 years ago
• MichaelArbel / GeneralizedEBM
  ☆53Updated 5 months ago
• mkhodak / relax
  ☆14Updated 2 years ago
• ermongroup / higher_order_invariance
  Code for "Accelerating Natural Gradient with Higher-Order Invariance"
  ☆30Updated 5 years ago
• necludov / action-matching
  Implementation of Action Matching for the Schrödinger equation
  ☆24Updated last year
• thegregyang / NNspectra
  Computing the eigenvalues of Neural Tangent Kernel and Conjugate Kernel (aka NNGP kernel) over the boolean cube
  ☆47Updated 5 years ago
• dylandoblar / noether-networks
  Meta-learning inductive biases in the form of useful conserved quantities.
  ☆37Updated 2 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆17Updated 3 years ago
• joneswack / dp-rfs
  This repository contains PyTorch implementations of various random feature maps for dot product kernels.
  ☆19Updated 6 months ago