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An illustrated explainer article on VQGAN, describing how the model generates images from text at a conceptual level and how VQGAN and CLIP work in tandem. It covers a theory of perception, using transformers to model interactions, expressing images through a codebook, and training the GAN and transformer.
MusicLM is a model that generates high-fidelity music from text descriptions. It frames conditional music generation as hierarchical sequence-to-sequence modeling and produces audio at 24 kHz consistent over several minutes. It can also be conditioned on text and a melody, transforming whistled or hummed melodies according to a text caption. The project released MusicCaps, a dataset of 5.5k music-text pairs with descriptions by human experts.
Optunity is a Python library containing various optimizers for hyperparameter tuning. It provides several approaches to solving tuning problems, including cross-validation facilities and score functions. The documentation states it is supported in R, MATLAB, GNU Octave and Java through Jython, and is free software under a BSD-style license.
An article by Andy Baio examining how AI companies use datasets and models from academic and nonprofit research groups. It describes Meta's Make-A-Video using Shutterstock clips and YouTube videos collected by Microsoft, and notes nonprofit datasets used for Stable Diffusion and other models. It argues this outsourcing shifts accountability and legal liability away from corporations.
A Hugging Face blog post introducing Matryoshka Embedding models. It explains embeddings as numerical representations and describes how Matryoshka models store more important information in earlier dimensions, allowing large embeddings to be truncated while remaining useful. It covers theoretical training and training with Sentence Transformers, with a comparison to a regular embedding model.
Accord.MachineLearning is a NuGet package, part of the Accord.NET Framework, that contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models, and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. Version 3.8.0 targets .NET Standard 1.4 and .NET Framework 3.5.
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An illustrated explainer article on VQGAN, describing how the model generates images from text at a conceptual level and how VQGAN and CLIP work in tandem. It covers a theory of perception, using transformers to model interactions, expressing images through a codebook, and training the GAN and transformer.
MusicLM is a model that generates high-fidelity music from text descriptions. It frames conditional music generation as hierarchical sequence-to-sequence modeling and produces audio at 24 kHz consistent over several minutes. It can also be conditioned on text and a melody, transforming whistled or hummed melodies according to a text caption. The project released MusicCaps, a dataset of 5.5k music-text pairs with descriptions by human experts.
Optunity is a Python library containing various optimizers for hyperparameter tuning. It provides several approaches to solving tuning problems, including cross-validation facilities and score functions. The documentation states it is supported in R, MATLAB, GNU Octave and Java through Jython, and is free software under a BSD-style license.
An article by Andy Baio examining how AI companies use datasets and models from academic and nonprofit research groups. It describes Meta's Make-A-Video using Shutterstock clips and YouTube videos collected by Microsoft, and notes nonprofit datasets used for Stable Diffusion and other models. It argues this outsourcing shifts accountability and legal liability away from corporations.
A Hugging Face blog post introducing Matryoshka Embedding models. It explains embeddings as numerical representations and describes how Matryoshka models store more important information in earlier dimensions, allowing large embeddings to be truncated while remaining useful. It covers theoretical training and training with Sentence Transformers, with a comparison to a regular embedding model.
Accord.MachineLearning is a NuGet package, part of the Accord.NET Framework, that contains Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models, and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. Version 3.8.0 targets .NET Standard 1.4 and .NET Framework 3.5.
