{"url":"/method/sse","slug":"sse","name":"SSE","full_name":"Stochastic Steady-state Embedding","full_name_withheld":false,"description_markdown":"Stochastic Steady-state Embedding (SSE) is an algorithm that can learn many steady-state algorithms over graphs. Different from graph neural network family models, SSE is trained stochastically which only requires 1-hop information, but can capture fixed point relationships efficiently and effectively.\r\n\r\nDescription and Image from: [Learning Steady-States of Iterative Algorithms over Graphs](https://proceedings.mlr.press/v80/dai18a.html)","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://icml.cc/Conferences/2018/Schedule?showEvent=2424","title":"Learning Steady-States of Iterative Algorithms over Graphs","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":41,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression","date":"2025-05-21","arxiv_id":"2505.15003","n_code_links":0,"syntology":null},{"paper":null,"title":"Impact of the COVID-19 pandemic on the financial market efficiency of price returns, absolute returns, and volatility increment: Evidence from stock and cryptocurrency markets","date":"2025-04-26","arxiv_id":"2504.18960","n_code_links":0,"syntology":null},{"paper":null,"title":"Millions of States: Designing a Scalable MoE Architecture with RWKV-7 Meta-learner","date":"2025-04-11","arxiv_id":"2504.08247","n_code_links":0,"syntology":null},{"paper":null,"title":"Image Coding for Machines via Feature-Preserving Rate-Distortion Optimization","date":"2025-04-03","arxiv_id":"2504.02216","n_code_links":0,"syntology":null},{"paper":null,"title":"Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering","date":"2025-01-09","arxiv_id":"2501.05165","n_code_links":0,"syntology":null},{"paper":null,"title":"Research on Optimal Portfolio Based on Multifractal Features","date":"2024-11-24","arxiv_id":"2411.15712","n_code_links":0,"syntology":null},{"paper":null,"title":"Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification","date":"2024-11-19","arxiv_id":"2411.12853","n_code_links":0,"syntology":null},{"paper":null,"title":"Efficient k-means with Individual Fairness via Exponential Tilting","date":"2024-06-24","arxiv_id":"2406.16557","n_code_links":0,"syntology":null},{"paper":null,"title":"Policy Iteration for Pareto-Optimal Policies in Stochastic Stackelberg Games","date":"2024-05-07","arxiv_id":"2405.06689","n_code_links":0,"syntology":null},{"paper":"/paper/inference-in-randomized-least-squares-and-pca","title":"Inference in Randomized Least Squares and PCA via Normality of Quadratic Forms","date":"2024-04-01","arxiv_id":"2404.00912","n_code_links":1,"syntology":null},{"paper":null,"title":"Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy","date":"2024-03-28","arxiv_id":"2403.19439","n_code_links":0,"syntology":null},{"paper":null,"title":"Dynamic Correlation of Market Connectivity, Risk Spillover and Abnormal Volatility in Stock Price","date":"2024-03-28","arxiv_id":"2403.19363","n_code_links":0,"syntology":null},{"paper":null,"title":"Joint Power Optimization and AP Selection for Secure Cell-Free Massive MIMO","date":"2024-01-12","arxiv_id":"2401.06859","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-clustering-via-structural","title":"Semi-Supervised Clustering via Structural Entropy with Different Constraints","date":"2023-12-18","arxiv_id":"2312.10917","n_code_links":1,"syntology":null},{"paper":"/paper/resnls-an-improved-model-for-stock-price","title":"ResNLS: An Improved Model for Stock Price Forecasting","date":"2023-12-02","arxiv_id":"2312.01020","n_code_links":1,"syntology":null},{"paper":null,"title":"SSE: A Metric for Evaluating Search System Explainability","date":"2023-06-16","arxiv_id":"2306.10175","n_code_links":0,"syntology":null},{"paper":"/paper/multi-step-ahead-stock-price-prediction-using","title":"Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition","date":"2022-12-24","arxiv_id":"2212.14687","n_code_links":1,"syntology":null},{"paper":null,"title":"Validation of Neural Network Controllers for Uncertain Systems Through Keep-Close Approach: Robustness Analysis and Safety Verification","date":"2022-12-13","arxiv_id":"2212.06532","n_code_links":0,"syntology":null},{"paper":null,"title":"ADMM based Distributed State Observer Design under Sparse Sensor Attacks","date":"2022-09-13","arxiv_id":"2209.06292","n_code_links":0,"syntology":null},{"paper":"/paper/optimization-of-decision-tree-evaluation","title":"Optimization of Decision Tree Evaluation Using SIMD Instructions","date":"2022-05-15","arxiv_id":"2205.07307","n_code_links":1,"syntology":null},{"paper":null,"title":"Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data","date":"2022-05-05","arxiv_id":"2205.02375","n_code_links":0,"syntology":null},{"paper":null,"title":"Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning","date":"2022-04-11","arxiv_id":"2204.05148","n_code_links":0,"syntology":null},{"paper":null,"title":"Differentiable and Scalable Generative Adversarial Models for Data Imputation","date":"2022-01-10","arxiv_id":"2201.03202","n_code_links":0,"syntology":null},{"paper":null,"title":"Pricing S&P 500 Index Options with Lévy Jumps","date":"2021-11-19","arxiv_id":"2111.10033","n_code_links":0,"syntology":null},{"paper":null,"title":"GCPG: A General Framework for Controllable Paraphrase Generation","date":"2021-10-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Rare event estimation using stochastic spectral embedding","date":"2021-06-09","arxiv_id":"2106.05824","n_code_links":0,"syntology":null},{"paper":null,"title":"Mahalanobis distance-based robust approaches against false data injection attacks on dynamic power state estimation","date":"2021-05-19","arxiv_id":"2105.08873","n_code_links":0,"syntology":null},{"paper":null,"title":"Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets","date":"2021-02-10","arxiv_id":"2102.05238","n_code_links":0,"syntology":null},{"paper":null,"title":"Learning Movement Strategies for Moving Target Defense","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/finrl-a-deep-reinforcement-learning-library","title":"FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance","date":"2020-11-19","arxiv_id":"2011.09607","n_code_links":6,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/time-series-1","name":"Time Series","papers":4},{"task":"/task/clustering","name":"Clustering","papers":3},{"task":"/task/prediction","name":"Prediction","papers":2},{"task":"/task/q-learning","name":"Q-Learning","papers":2},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":2},{"task":"/task/state-estimation","name":"State Estimation","papers":2},{"task":"/task/stock-price-prediction","name":"Stock Price Prediction","papers":2},{"task":"/task/time-series","name":"Time Series Analysis","papers":2},{"task":"/task/active-learning","name":"Active Learning","papers":1},{"task":"/task/adversarial-attack","name":"Adversarial Attack","papers":1},{"task":"/task/binarization","name":"Binarization","papers":1},{"task":null,"name":"CPU","papers":1},{"task":"/task/collaborative-filtering","name":"Collaborative Filtering","papers":1},{"task":"/task/collaborative-ranking","name":"Collaborative Ranking","papers":1},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/decision-making","name":"Decision Making","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/deep-reinforcement-learning","name":"Deep Reinforcement Learning","papers":1},{"task":"/task/distributed-computing","name":"Distributed Computing","papers":1},{"task":"/task/fairness","name":"Fairness","papers":1}],"tasks_shown":20,"n_tasks":36,"usage_by_year":[{"year":"2018","papers":3},{"year":"2019","papers":4},{"year":"2020","papers":5},{"year":"2021","papers":6},{"year":"2022","papers":7},{"year":"2023","papers":3},{"year":"2024","papers":8},{"year":"2025","papers":5}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/sse"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}