Popular GitHub Repositories for Piecewise Constant

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.

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Extend existing LLMs way beyond the original training length with constant memory usage, without retraining

Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

Unsupervised multilabel image segmentation (color/gray/multichannel) based on the Potts model (aka piecewise constant Mumford-Shah model)

High order and sparse layers in pytorch. Lagrange Polynomial, Piecewise Lagrange Polynomial, Piecewise Discontinuous Lagrange Polynomial (Chebyshev nodes) and Fourier Series layers of arbitrary order. Piecewise implementations could be thought of as a 1d grid (for each neuron) where each grid element is Lagrange polynomial. Both full connected and convolutional layers included.

Reference implementation of "Softmax Attention with Constant Cost per Token" (Heinsen, 2024)

APLR 使用自动分段线性回归构建可预测且易于解释的回归和分类模型。它在预测精度方面经常与基于树的方法不相上下，同时提供更平滑且易于解释的预测结果。

A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

Piecewise Flat Embedding for Image Segmentation

Supplementary material for IEEE Services Computing paper 'An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware'

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Functional Latent datA Models for clusterING heterogeneOus curveS

A Testing Framework for Decision-Optimization Model Learning Algorithms

Implementation of "Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks" by Fang et al. (2020)

Diacritics are short vowels with a constant length that are spoken. The same word in the Arabic language can have different meanings and different pronunciations based on how it is diacritized. In this project, we implement a pipeline to predict the diacritic of each character in an Arabic text using Natural Language Processing techniques.

Experiments in language interpolation with high order sparse neural networks

Leveraging network motifs to improve artificial neural networks