Showing 8 open source projects for "input-output model"

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  • 1
    AIMET

    AIMET

    AIMET is a library that provides advanced quantization and compression

    Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware...
    Downloads: 32 This Week
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  • 2
    ncnn

    ncnn

    High-performance neural network inference framework for mobile

    ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including...
    Downloads: 28 This Week
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  • 3
    Flax

    Flax

    Flax is a neural network library for JAX

    ...Tutorials and examples show patterns for multi-host training, mixed precision, and advanced input pipelines that scale from laptops to TPUs.
    Downloads: 0 This Week
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  • 4
    Sonnet

    Sonnet

    TensorFlow-based neural network library

    Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already ship with Sonnet, making it quite powerful and yet simple at the same time. Users are also encouraged to build their own modules. ...
    Downloads: 0 This Week
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  • 5
    SVoice (Speech Voice Separation)

    SVoice (Speech Voice Separation)

    We provide a PyTorch implementation of the paper Voice Separation

    ...This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. Separate models are trained for different speaker counts, and the largest-capacity model dynamically determines the actual number of speakers in a mixture. ...
    Downloads: 0 This Week
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  • 6
    char-rnn

    char-rnn

    Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)

    char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. ...
    Downloads: 0 This Week
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  • 7
    DeepDream

    DeepDream

    This repository contains IPython Notebook with sample code

    DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing gradients with respect to the input image, and applying multi-scale “octave” processing to reveal fine and coarse patterns. ...
    Downloads: 0 This Week
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  • 8

    Fast Matrix for Java

    General purpose matrix utilities for Java in Parallel Computing

    Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors.
    Downloads: 0 This Week
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