Search Results for "norm"
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Showing 10 open source projects for "norm"
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llm-abliteration
Make abliterated models with transformers, easy and fast
...Batch inference and quantized model loading help reduce processing requirements. Sharded weight modifications allow larger models to be processed with limited memory. Advanced options include norm-preserving transformations, directional projection, and configurable layer strategies. Modified models can be tested and compared, although refusal removal is not guaranteed. -
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MNE-Python
Magnetoencephalography (MEG) and Electroencephalography EEG in Python
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more. -
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Bootstrap Your Own Latent (BYOL)
Usable Implementation of "Bootstrap Your Own Latent" self-supervised
...This repository offers a module that one can easily wrap any image-based neural network (residual network, discriminator, policy network) to immediately start benefitting from unlabelled image data. There is now new evidence that batch normalization is key to making this technique work well. A new paper has successfully replaced batch norm with group norm + weight standardization, refuting that batch statistics are needed for BYOL to work. Simply plugin your neural network, specifying (1) the image dimensions as well as (2) the name (or index) of the hidden layer, whose output is used as the latent representation used for self-supervised training. -
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DeepSeek LLM
DeepSeek LLM: Let there be answers
...The model is trained from scratch, reportedly on a vast multilingual + code + reasoning dataset, and competes with other open or open-weight models. The architecture mirrors established decoder-only transformer families: pre-norm structure, rotational embeddings (RoPE), grouped query attention (GQA), and mixing in languages and tasks. It supports both “Base” (foundation model) and “Chat” (instruction / conversation tuned) variants. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll... -
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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dnrDALMAS
A general-level Prolog implementation of the DALMAS architecture.
...University of Gävle, Division of Computer Science; University of Gävle. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-1475 See also: Odelstad, J., & Boman, M. (2004). Algebras for Agent Norm-Regulation. Annals of Mathematics and Artificial Intelligence, 42(1), 141–166. http://doi.org/10.1023/B:AMAI.0000034525.49481.4a -
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lonestar
A feature selection and classification algorithm based on L1 Norm SVM
A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination. -
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2OPL
2OPL is a norm programming language for agent organizations.
...Effect rules describe how actions change the brute facts. Norms describe which environment states counts as violations. Finally sanction rules can be programmed to respond to norm violations. The current implementation is based on logic programming (Prolog in particular). For those users who are not familiar with Prolog we advise the free ebook Learn Prolog Now (http://www.learnprolognow.org). The interpreter supports three different norm specifications. For more information see the documentation or mail one of the developers. -
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nu-BDI is an extension of the Jason agent interpreter aimed at processing norms using constraints on the agent's instantiated plans. The core agent reasoning cycle now includes a component for processing norms and enforcing norm constraints.
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