Search Results for "meta" - Page 3
Sort By:
Showing 73 open source projects for "meta"
View related business solutions-
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
1
Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
Replica Dataset is a high-quality 3D dataset of realistic indoor environments designed to advance research in computer vision, robotics, and embodied AI. Developed by Facebook Research (now Meta AI), it features accurate geometric reconstructions, high-resolution and high dynamic range textures, and comprehensive semantic annotations. Each environment contains detailed models of real-world spaces, including rooms, furniture, glass, and mirror surfaces. The dataset also provides semantic and instance segmentations, planar decomposition, and navigation meshes, making it highly suitable for simulation, visual perception, and autonomous navigation tasks. ... -
2
Meta-Learning-Papers
Meta Learning/Learning to Learn/One Shot Learning/Few Shot Learning
Meta-Learning-Papers is a curated bibliography focused specifically on meta-learning, learning-to-learn, one-shot learning, and few-shot learning, intended for researchers and practitioners interested in this rapidly evolving subfield of machine learning. It catalogs foundational “legacy” papers that introduced key concepts, as well as more recent work that extends meta-learning to new domains or architectures. -
3
LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
LearningToCompare_FSL is a PyTorch implementation of the “Learning to Compare: Relation Network for Few-Shot Learning” paper, focusing on the few-shot learning experiments described in that work. The core idea implemented here is the relation network, which learns to compare pairs of feature embeddings and output relation scores that indicate whether two images belong to the same class, enabling classification from only a handful of labeled examples. The repository provides training and... -
4
popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known network flow and other graph algorithms. ... -
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.
-
5
Cpt Kirk
Obtain why and why-not justifications for answer set programs.
Since the method proposed in https://www.researchgate.net/publication/262599199_Unifying_Provenance_and_Debugging_for_Answer-Set_Programs?ev=prf_pub is based on meta-programming, it is possible to use existing state-of-the-art software systems that support well-founded and answer-set semantics, which allowed us to start developing this new tool by extending the one that exists related to a debugging approach: Spock, hence the name Cpt. Kirk. Furthermore and more importantly, one direction to explore is to use the technique of reification as described in "metaASP" to obtain the implicants via a saturation technique, and obtaining the prime implicants of provenance formulae by optimization and thus proper minimal justifications. -
6
MetaNet
Free portable library for meta neural network research
MetaNet provides free library for meta neural network research. MetaNet library contain feed-forward neural net realisation and several integrated dataset (MNIST). -
7
Service oriented framework for Evolutionary Algorithms and other meta-heuristics using OSGi.
-
8
INGENIAS Development Kit (IDK) is a framework for analysis, design and implementation of multi-agent systems (MAS). It is based on the specification of MAS meta-models, from which tools such as the model editor and code generation are generated.
-
9
TML - Text Mining Library for LSA & CMM
TML is a Java Library for LSA and extracting Concept Maps from text
TML has moved to http://www.villalon.cl/tml.html and the code to https://github.com/villalon/tml -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
10
Search Agents - a framework for collaboration of meta-heuristic agents
-
11
SURIKATA (Syntactic Universal Reasoning for Inducing Kolmogorov Abstract Theories Automatically) is a system for searching large spaces of artifacts and inducing algorithms for generating similar artifacts.
-
12
Spock is a prototype tool for debugging logic programs under the answer-set semantics. It provides debugging methods for propositional programs in DLV or Smodels syntax. The implemented techniques rely on ASP-meta-programming.
-
13
JOFS is a java project concerning the develop of an owl ontology to store meta informations about files and documents. It will provide a core to easily manage the ontology and an architecture to easily add to the core functionalities.
-
14
META is a decision making software which aims are to track computer attackers, computer attacks and to help investigators finding useful elements.
-
15
The Text Annotation Environment (tae) can be used to annotate natural language text manually or automatically (UIMA Annotator) with meta information (tokens, part-of-speech, named entities, ...). Tae is based on Eclipse and IBM's UIMA.
-
16
MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.
-
17
MAIF is developed in Java 5 (especially Generics) and aims at building AI algorithms, by concentrating onto the mapping of real-world problems, while abstracting from their inner working. It can be extended with new algorithms and problem representations.
-
18
A java framework for developing meta-heuristics that supports the use of grids environments. The meta-heuristics planned to be realeased are GAs, VNS and NNs. The grid middleware that will be firstly explored is the OurGrid solution.
-
19
META Spider is a neural network powered search engine which aim is to determine if a given raw information is relevant, using the experience of the active user of the hosting computer as a reference.
-
20
Llama-3.2-1B
Llama 3.2–1B: Multilingual, instruction-tuned model for mobile AI
meta-llama/Llama-3.2-1B is a lightweight, instruction-tuned generative language model developed by Meta, optimized for multilingual dialogue, summarization, and retrieval tasks. With 1.23 billion parameters, it offers strong performance in constrained environments like mobile devices, without sacrificing versatility or multilingual support. -
21
This projects implements various optimization heuristics and meta-heuristics (such as local search, VND, GRASP, Simulated Annealing, and more still to come) finding solutions on the post enrolment course timetabling problem.
-
22
bart-large-cnn
Summarization model fine-tuned on CNN/DailyMail articles
facebook/bart-large-cnn is a large-scale sequence-to-sequence transformer model developed by Meta AI and fine-tuned specifically for abstractive text summarization. It uses the BART architecture, which combines a bidirectional encoder (like BERT) with an autoregressive decoder (like GPT). Pre-trained on corrupted text reconstruction, the model was further trained on the CNN/DailyMail dataset—a collection of news articles paired with human-written summaries. -
23
Llama-3.2-1B-Instruct
Instruction-tuned 1.2B LLM for multilingual text generation by Meta
Llama-3.2-1B-Instruct is Meta’s multilingual, instruction-tuned large language model with 1.24 billion parameters, optimized for dialogue, summarization, and retrieval tasks. It builds upon the Llama 3.1 architecture and incorporates fine-tuning techniques like SFT, DPO, and quantization-aware training for improved alignment, efficiency, and safety. The model supports eight primary languages (including English, Spanish, Hindi, and Thai) and was trained on a curated mix of publicly available...
