Search Results for "learning vector quantization"
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Showing 15 open source projects for "learning vector quantization"
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1
AIMET
AIMET is a library that provides advanced quantization and compression
...Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers,... -
3
Tile Kernels
A kernel library written in tilelang
Tile Kernels is a DeepSeek kernel library written with TileLang for high-performance AI and machine-learning workloads. It contains specialized kernels for areas such as mixture-of-experts routing, quantization, batched transpose operations, Engram gating, and Manifold HyperConnection components. The project includes both optimized kernel implementations and PyTorch reference versions for comparison and validation. It is aimed at developers and researchers who work close to model internals and need efficient low-level building blocks. ... -
4
RuVector
Self-Learning, Vector Graph Neural Network, and Database built in Rust
RuVector is part of the broader rUv ecosystem of AI engineering tools and focuses on enabling advanced vector-based processing and intelligent system development within agentic and AI-driven pipelines. The project fits into a larger vision of modular, composable AI infrastructure designed to support autonomous agents, data retrieval, and intelligent automation workflows. It emphasizes extensibility and interoperability with modern AI stacks, allowing developers to integrate vector operations... -
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5
XNNPACK
High-efficiency floating-point neural network inference operators
XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch... -
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DeepMatch
A deep matching model library for recommendations & advertising
DeepMatch is an open-source deep matching library built for recommendation and advertising systems. It helps developers train models that learn vector representations for users and items. These representations can be exported and used in approximate nearest neighbor search for large-scale retrieval. The library supports familiar Keras workflows through model.fit() and model.predict(). Its model collection includes FM, DSSM, YouTubeDNN, NCF, SDM, MIND, and ComiRec. It is designed to make... -
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Earth Engine API
Python and JavaScript bindings for calling the Earth Engine API
The Earth Engine API provides Python and JavaScript client libraries for Google Earth Engine, a planetary-scale geospatial analysis platform. With it, users compose lazy, server-side computations over massive catalogs of satellite imagery and vector datasets without handling raw files locally. The API exposes functional operators for map algebra, reducers, joins, and machine learning that scale transparently on Earth Engine’s backend. Developers authenticate once, work interactively in notebooks or the Code Editor, and export results to Cloud Storage, Drive, or asset collections. ... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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nlp_chinese_corpus
Large Scale Chinese Corpus for NLP
...Each dataset includes descriptions, download links, structure notes, and examples to help users understand how the data is formatted. The corpora can support tasks such as language model pretraining, word vector training, question answering, title generation, keyword generation, translation, and sentence representation learning. Overall, it is a practical resource hub for building or testing Chinese NLP models with larger and more varied datasets. -
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10
MITIE
MITIE: library and tools for information extraction
...The current release includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors. MITIE is built on top of dlib, a high-performance machine-learning library[1], MITIE makes use of several state-of-the-art techniques including the use of distributional word embeddings[2] and Structural Support Vector Machines[3]. MITIE offers several pre-trained models providing varying levels of support for both English, Spanish, and German trained using a variety of linguistic resources (e.g., CoNLL 2003, ACE, Wikipedia, Freebase, and Gigaword). ... -
11
NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. -
14
node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
15
libVMR
VMR - machine learning library
libVMR is a class library written in Java which implements code generator for group method of data handling - GMDH. The library is intended for users, with machine learning skills. libVMR provides an effective framework for the research and development of data mining and predictive analytics. libVMR is based on the most popular neural network model with a higher generalization ability from kernel tricks - vector machine by Reshetov (VMR). The library has been designed to learn from data sets. ...
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