Search Results for "properties" - Page 2
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Showing 51 open source projects for "properties"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Termin-Bot
Telegram bot to get notifications about free appointments
Termin (eng. appointment) is essential in Germany, especially in Berlin. Sometimes it is tough to get a termin in public institutions and this bot is a way to automate termin checks and send Telegram notifications, if free termin was found. The code could be also considered as a template for subsequent development. The principle of bot operation is to refresh a webpage with needed appointments periodically [TERMIN_URL] and to recognize the change on it. E.g., the page always contains the... -
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Diffusers-Interpret
Model explainability for Diffusers
diffusers-interpret is a model explainability tool built on top of Diffusers. Model explainability for Diffusers. Get explanations for your generated images. Install directly from PyPI. It is possible to visualize pixel attributions of the input image as a saliency map. diffusers-interpret also computes these token/pixel attributions for generating a particular part of the image. To analyze how a token in the input prompt influenced the generation, you can study the token attribution scores.... -
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mlr
Machine Learning in R
R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods,... -
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ConvNeXt
Code release for ConvNeXt model
ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure... -
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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
...As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects. -
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aiode
Discord bot that plays Spotify tracks and YouTube videos or any URL
...Create cross-platform playlists with tracks from any source. Simple and customizable player commands. Create custom command presets as shortcuts for your most used commands. Adjustable properties for even deeper customization. Sign in to Spotify to play your own playlists or upload aiode playlists. Manage what roles can access which commands. Customize how you want to summon your bot by using a custom prefix or giving your bot a name. Advanced admin commands such as updating and rebooting the bot or cleaning up the database available to bot administrators. ... -
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Surface Defect Detection Dataset Papers
Constantly summarizing open source dataset and critical papers
...Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. -
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Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
...Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately. In this paper, we show that even the latest version of the precision and recall (Kynkäänniemi et al., 2019) metrics are not reliable yet. For example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. ... -
9
Dynamic Routing Between Capsules
A PyTorch implementation of the NIPS 2017 paper
...Capsule networks aim to improve how neural models represent spatial hierarchies and relationships between objects within images. Instead of scalar neuron activations, capsules output vectors that encode both the presence of features and their spatial properties such as orientation or pose. The repository implements the dynamic routing algorithm between capsules, which allows lower-level features to route their outputs to higher-level structures that best represent the detected patterns. This approach enables the model to capture part-to-whole relationships in visual data more effectively than standard CNNs. ... -
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AI-Blocks
A powerful and intuitive WYSIWYG to create Machine Learning models
...The model can be run directly on the editor or be exported to a standalone script that runs on Tensorflow. Variables are parsed from python scripts and can be edited from the AI-Blocs properties panel. To run your model simply press the "Play" button and let the magic happen! The project requires Python and Tensorflow to run projects. You can still create and edit projects without these dependencies. To run AI-Blocs, download the project archive and launch AI-Blocs.exe. -
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This paper represent a development and deployment and/or Implementation of Optical Character Recognition (OCR) to translate images of typewritten or handwritten characters into electronically editable format by preserving font properties. OCR can do this by applying pattern matching algorithm. The Recognized characters are stored in editable format. Thus OCR make the computer read the printed documents discarding noise. Keywords- Optical Character Recognition, Image convert to character, Image cropping.
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
...We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them intuitive. -
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DoAllWithPDF_servicemenu
KDE servicemenu for pdf
allows kde user to make a lot of things whit right click on a pdf file. -
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logmap-matcher
LogMap: An Ontology Alignment and Alignment Repair System
LogMap is a highly scalable ontology matching system with ‘built-in’ reasoning and inconsistency repair capabilities. LogMap extract mappings between classes, properties and instances. To the best of our knowledge, LogMap is one of the few matching systems that: 1. can efficiently match semantically rich ontologies containing tens (and even hundreds) of thousands of classes, 2. incorporates sophisticated reasoning and repair techniques to minimise the number of logical inconsistencies, and 3. provides support for user intervention during the matching process (see Web interface). -
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HLearn
Homomorphic machine learning
HLearn is a Haskell-based machine learning library focused on composability, algebraic structure, and performance. It provides a functional approach to building machine learning algorithms by leveraging algebraic properties such as monoids and groups. This allows for parallel, incremental, and distributed computation in a mathematically consistent way. HLearn aims to provide implementations of common algorithms like k-means, naive Bayes, and others while maintaining the expressiveness and safety of the Haskell language. -
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Human AI Net
a Human and Artificial Intelligence Network
...The main data format is, from xorlisp which is also in progress, immutable binary forest nodes, so if millions of people build that together nobody can damage or change anyone else's data since its all constant. You dont change variables. You create new data that points at existing constant data, as deep as you need it. I have mindmap lists, definitions, and 2 editable properties working that way with 2 kinds of event listeners that work locally. Its not a networked system yet, but the datastructs are ready to scale with it. -
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EpochX is an open source genetic programming framework, specifically for analysing the properties of evolutionary automatic programming. It supports 3 popular representations - Strongly-Typed GP, Context-Free Grammar GP and Grammatical Evolution.
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BayesianCortex
simple algorithm for a realtime interactive visual cortex for painting
A paint program where the canvas is the visual cortex of a simple kind of artificial intelligence. You paint with the mouse into its dreams and it responds by changing what you painted gradually. There will also be an API for using it with other programs as a general high-dimensional space. Each pixel's brightness is its own dimension. Bayesian nodes have exactly 3 childs because that is all thats needed to do NAND in a fuzzy way as Bayes' Rule which is NAND at certain extremes. NAND can be... -
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...Yann is a library to create fast neural networks. It is also a GUI to easily create, edit, train, execute and investigate networks. Multiple topologies, runtime properties and ensemble learning are supported.
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This project develops a web-based (JSP) Fuzzy Rule-Based Expert System for analyzing ECG (electro cardio gram) signals & diagnosing Tachi-Arrhythmias. Proj. main blocks: inference engine, knowledgebase, KB editor, explanation, and feature extraction.
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The goal of this project is to build a very large ontology. What is intended is a multiple inheritence taxonomy with properties. Usual ontologies like OpenCyc or ThoughTreasure weights a few megabytes. The project will build a 100 megabyte ontology.
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This project simulates a multi-agent system (swarm) behavior both graphically and not. The purpose of this project is to research the properties suggested in "stability analysis of swarms" V.Gazi & K.M.Passino. Using the vpython library for 3D modeling
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A DAML+OIL ontology editor with constraint propagation functionality to ensure that constraints applied to properties and restrictions are correctly propagated through an ontology, and datatype management functionality for manipulating custom datatypes.
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Leanstral
Open-source code agent designed for Lean 4
...It leverages modern large-scale architectures, likely incorporating mixture-of-experts techniques, to balance efficiency and capability while handling structured symbolic reasoning tasks. The model can assist in writing proofs, exploring mathematical structures, and validating logical properties in code. -
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Procedural content generation of deterministic and complex entities, with properties and other entities inside, defined by an editable XML file, along with a framework to simulate actions, compositions, and interactions of entities.