Popular GitHub Repositories for Hierarchical Softmax

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

A Collection of Variational Autoencoders (VAE) in PyTorch.

Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

Scalable and user friendly neural :brain: forecasting algorithms.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

HAAS = Hierarchical Autonomous Agent Swarm - "Resistance is futile!"

behaviac is a framework of the game AI development, and it also can be used as a rapid game prototype design tool. behaviac supports the behavior tree, finite state machine and hierarchical task network(BT, FSM, HTN)

[ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior

Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

A PyTorch Toolbox for Face Recognition

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

Text Classification Algorithms: A Survey

Implementation for <SphereFace: Deep Hypersphere Embedding for Face Recognition> in CVPR'17.

Bi-directional Attention Flow (BiDAF) network is a multi-stage hierarchical process that represents context at different levels of granularity and uses a bi-directional attention flow mechanism to achieve a query-aware context representation without early summarization.

For deep RL and the future of AI.

Code for the Lovász-Softmax loss (CVPR 2018)

[ICLR'23 Spotlight?] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"