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rlkit

Collection of reinforcement learning algorithms

robuststabilityguaranteerl

RobustStabilityGuaranteeRL

ross

ROSS is a library written in Python for rotordynamic analysis.

rotating-machine-fault-data-set

Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)

s2m2_fewshot

s3prl

Self-Supervised Speech Pre-training and Representation Learning Toolkit.

s4torch

PyTorch implementation of Structured State Space for Sequence Modeling (S4), based on Annotated S4.

sac-her

Implementation of Soft Actor-Critic with Hindsight Experience Replay

sac-lagrangian

PyTorch implementation of Constrained Reinforcement Learning for Soft Actor Critic Algorithm

sac-qmix

Algorithm that combines QMIX with SAC for Multi-Agent Reinforcement Learning.

sac_discrete

PyTorch implementation of the discrete Soft-Actor-Critic algorithm.

safety-starter-agents

Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.

scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

sgconv

similarityrul

基于相似性的剩余寿命预测

simplified-dsn

Simplified implementation for Domain Seperation Networks

skab

SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.

snopt

Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight

snr

Style Normalization and Restitution for Domain Generalization and Adaptation

soft-actor-critic

Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.

sonode

Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"

speech_signal_processing_and_classification

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) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

ssr

(NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductive few-shot classification"

state-spaces

Sequence Modeling with Structured State Spaces

statistical-learning-method_code

手写实现李航《统计学习方法》书中全部算法

swin-transformer

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

symbolicphysicslearner

Symbolic physics learner: Discovering governing equations via Monte Carlo tree search

tent

ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization

tf2multiagentrl

Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x

tlada