Popular GitHub Repositories for Proper Orthogonal Decomposition

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

TensorLy: Tensor Learning in Python.

StableSR for Stable Diffusion WebUI - Ultra High-quality Image Upscaler

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

[CVPR 2025?] Identity-Preserving Text-to-Video Generation by Frequency Decomposition

Unsupervised Speech Decomposition Via Triple Information Bottleneck

PyTorch Implementation of EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

Tensorflow tutorial for various Deep Neural Network visualization techniques

Tidy anomaly detection

Tensor Network Learning with PyTorch

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

Official Code for "SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression"

List of awesome papers on Intrinsic Decomposition & Inverse Rendering

DataGene - Identify How Similar TS Datasets Are to One Another (by @firmai)