Broaden your selection: Category/Science
- will offer search and tools to reduce your data, keep it clean, fast and easy. in alpha development stage.
- In short, Aletheia is software for getting science published and into the hands of everyone, for free. It's a decentralised and distributed database used as a publishing platform for scientific research. So, Aletheia is software. But software without people is nothing. To comprehensively answer the question what is Aletheia, Aletheia is software surrounded by a community of people who want to change the world through open access to scientific knowledge. For a more in depth explanation, Aletheia is an Ethereum Blockchain application utilising IPFS for decentralised storage that anyone can upload documents to, download documents from, that also handles the academic peer review process. The application runs on individual PCs, all forming part of the IPFS database. This gives us an open source platform that cannot be bought out by the large publishers (and any derivitive works must also be open source) that should also be hard to take down due to the database being spread across the globe in multiple legal jurisdictions. Aletheia is designed to be a resilient platform run transparently by the community, not some black box corporation or editorial board, meaning all users can see the decisions Aletheia is making and have a stake in that decision making process if they so desire. By this nature, Aletheia is decentralised, it has no key person risk. Should the core group who invented Aletheia dissapear Aletheia won't cease to exist, it will continue to be run by the community. The community moderates content through various mechanisms (peer review, reputation scores etc.,) to ensure quality of content.
- Charlemagne is a genetic programming application that includes both a commandline client and an interactive console mode. It is written in Python and Lisp, and is user extensible to some degree in both languages. It features built-in input-output mapping support and provides the ability to define complex fitness calculations in Lisp or Python.
- Based on paper: Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and the training library JAXFORMER.
- Based on paper: CodeGen2 is a family of autoregressive language models for program synthesis.
- 'dbacl' is a digramic Bayesian text classifier. Given some text, it calculates the posterior probabilities that the input resembles one of any number of previously learned document collections. It can be used to sort incoming email into arbitrary categories such as spam, work, and play, or simply to distinguish an English text from a French text. It fully supports international character sets, and uses sophisticated statistical models based on the Maximum Entropy Principle.
- 'Deduce' is an artificial intelligence program which accepts natural language sentences as input. These sentences describe properties and relationships between objects, (for example, "Spot is a dog", "A liquid will evaporate", or "Water does not flow uphill"). The user can then ask questions against that input, to which Deduce will attempt to answer using deductive reasoning techniques.
- From GitHub README: DeepSpeech is an Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier.
- The Dezyne language has formal semantics expressed in mCRL2 developed at the department of Mathematics and Computer Science of the Eindhoven University of Technology (TUE). Dezyne requires that every model is finite, deterministic and free of deadlocks, livelocks, and contract violations. This achieved by means of the language itself as well as by builtin verification through model checking. This allows the construction of complex systems by assembling independently verified components.
- Primary aim of the dinrhiw is to be linear algebra library and machine learning
library. For this reason dinrhiw implements PCA and neural
network codes. Currently, the neural network code only supports:
- hamiltonian monte carlo sampling (HMC) and simple bayesian neural network
- second order L-BFGS search
- gradient descent (backpropagation)
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