hydra-zen VS memorization

Compare hydra-zen vs memorization and see what are their differences.

hydra-zen

Create powerful Hydra applications without the yaml files and boilerplate code. (by mit-ll-responsible-ai)
Configuration Machine Learning dataclasses dynamic-configuration yaml-configuration Pytorch pytorch-lightning Reproducibility reproducible-science Scalable Configurable Machinelearning Deep Learning
Source Code
mit-ll-responsible-ai.github.io
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memorization

Code for "On Memorization in Probabilistic Deep Generative Models" (by alan-turing-institute)
Deep Learning generative-model probabilistic-models Pytorch reproducible-research Reproducibility reproducible-science reproducible-paper memorization
Source Code
papers.nips.cc
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hydra-zen memorization
1 1
281 5
7.1% -
9.4 10.0
2 days ago about 2 years ago
Python Python
MIT License MIT License
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hydra-zen

Posts with mentions or reviews of hydra-zen. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-02.

memorization

Posts with mentions or reviews of memorization. We have used some of these posts to build our list of alternatives and similar projects.
  • [D] DALL·E to be made available as API, OpenAI to give users full ownership rights to generated images
    1 project | /r/MachineLearning | 4 Nov 2022
    Codex is not technically copy pasting; it is generating a new output that is (almost) exactly the same, or indistinguishable on the eyes of a human, to the input. Sounds like semantics, but there is no actual copying. You already have music generating algorithms that can also generate short samples that are indistinguishable to the inputs (memorisation). Dall-E 2 is not there yet, but we are close to prompting "Original Mona Lisa painting" and be given back the original Mona Lisa painting with striking similarities. There are already several generative models of images that can mostly memorise inputs used to train it (quick example found using google: https://github.com/alan-turing-institute/memorization).

What are some alternatives?

When comparing hydra-zen and memorization you can also consider the following projects:

kubeproject - A set of tools built to simplify daily driving of cloud resources for individual VM access, Kubernetes batch jobs and miscellaneous useful functionality related to cloud-based ML research

torch-fidelity - High-fidelity performance metrics for generative models in PyTorch

traingenerator - 🧙 A web app to generate template code for machine learning

wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

forward-forward-pytorch - Forward Forward algorithm (by Geoffrey Hinton) implemented with pytorch.

Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

minimal-ml-template - A very minimal ml project template that uses HF transformers and wandb to train a simple NN and evaluate it, in a stateless manner compatible with Spot instances kubernetes workflows

benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

disentangling-vae - Experiments for understanding disentanglement in VAE latent representations

pytorch-forecasting - Time series forecasting with PyTorch

PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.

hydra-zen vs kubeproject memorization vs torch-fidelity hydra-zen vs traingenerator memorization vs wandb hydra-zen vs forward-forward-pytorch memorization vs Sacred hydra-zen vs minimal-ml-template memorization vs benchmark_VAE hydra-zen vs lightning-hydra-template memorization vs disentangling-vae hydra-zen vs pytorch-forecasting memorization vs PyTorch-VAE