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Fast Differentiable Forest lib with the advantages of both decision trees and neural networks
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
A concise but complete full-attention transformer with a set of promising experimental features from various papers
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
A python library for decision tree visualization and model interpretation.
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning