akandykeller / NeuralWaveMachinesLinks
Official Implementation of the ICML 2023 paper: "Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks"
☆72Updated 2 years ago
Alternatives and similar repositories for NeuralWaveMachines
Users that are interested in NeuralWaveMachines are comparing it to the libraries listed below
Sorting:
- Brain-like variational inference☆57Updated 3 months ago
- Hierarchical Associative Memory User Experience☆103Updated last month
- Meta-learning inductive biases in the form of useful conserved quantities.☆37Updated 2 years ago
- Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable.☆172Updated 2 years ago
- Quantify geometric intelligence in natural and artificial brains.☆51Updated 2 months ago
- Official repository of "Spontaneous symmetry breaking in generative diffusion models"☆43Updated last year
- The Energy Transformer block, in JAX☆59Updated last year
- ☆60Updated 3 years ago
- ☆191Updated 2 months ago
- Quantification of Uncertainty with Adversarial Models☆30Updated 2 years ago
- ☆33Updated 2 years ago
- Graph neural networks in JAX.☆67Updated last year
- Flexible Inference for Predictive Coding Networks in JAX.☆53Updated last month
- Official repository for the paper "A Modern Self-Referential Weight Matrix That Learns to Modify Itself" (ICML 2022 & NeurIPS 2021 Deep R…☆173Updated 2 months ago
- Software for using predictive coding algorithms to train PyTorch models.☆26Updated last year
- ☆43Updated 3 weeks ago
- Implicit Convolutional Kernels for Steerable CNNs [NeurIPS'23]☆29Updated 6 months ago
- ☆54Updated 2 years ago
- Official PyTorch and JAX Implementation of "Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks"☆38Updated last year
- Composable kernels for scikit-learn implemented in JAX.☆44Updated 4 years ago
- ☆40Updated 3 years ago
- Sequence Modeling with Multiresolution Convolutional Memory (ICML 2023)☆125Updated last year
- Automatic Integration for Neural Spatio-Temporal Point Process models (AI-STPP) is a new paradigm for exact, efficient, non-parametric inf…☆24Updated 10 months ago
- Fast singularity detection with kernel☆36Updated last year
- DiCE: The Infinitely Differentiable Monte-Carlo Estimator☆31Updated 2 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Diffusion models in PyTorch☆107Updated 2 months ago
- ☆106Updated 3 years ago
- Easy Hypernetworks in Pytorch and Jax☆104Updated 2 years ago
- Code for "Meta Learning Backpropagation And Improving It" @ NeurIPS 2021 https://arxiv.org/abs/2012.14905☆33Updated 3 years ago