FARGonautica
AI summary
Concept models
An effort to collect and make accessible various computational architectures related to fluid concepts and creative analogies.
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Generative model framework
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ML models
A collection of pre-trained machine learning models for use in web applications.
Concept explanation tool
An interpretability method that provides explanations for neural network predictions by highlighting high-level concepts relevant to classification tasks.
Vision-Language Model
A multimodal AI model that enables real-world vision-language understanding applications
Formal System Simulator
An implementation of formal systems from Douglas Hofstadter's book Gödel, Escher, Bach
Visual concept detection framework
This codebase provides a framework for detecting visual concepts in images by leveraging image captions and pre-trained models.
3D LLM
Developing a Large Language Model capable of processing 3D representations as inputs
Deep Learning Models
Provides a collection of deep learning models and utilities in JAX/Flax for research purposes.
Drug discovery model
Investigating neural networks for drug discovery using multiple chemical descriptors.
Artificial life simulator
A PyTorch-based framework for training and studying artificial species in bio-inspired environments
Document comprehension model
An implementation of a vision vocabulary model for large language models to improve document understanding and recognition capabilities
Financial models
Provides basic financial market objects and methods to model key financial concepts in R.
Deep learning framework bridge
An abstraction layer between various deep learning frameworks and your program.
lxtgh/omg-seg1.3K
Visual Model
Develops an end-to-end model for multiple visual perception and reasoning tasks using a single encoder, decoder, and large language model.
ML models
Provides pre-trained machine learning models for natural language processing tasks using Clojure and the clj-djl framework.
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