Do literature survey on quasi-newton methods for machine learning?
Best insight from top research papers
Quasi-Newton methods have been widely studied in the context of machine learning. These methods aim to optimize the convergence rate and efficiency of optimization algorithms. One approach is to use stochastic variants of quasi-Newton methods, which construct Hessian approximations using only gradient information. BFGS-based methods in stochastic settings have been particularly focused on, with algorithmic improvements to enhance applicability, lower computational and storage costs, and improve convergence rates. Another approach is to develop faster stochastic quasi-Newton methods that leverage second-order information. SpiderSQN is a novel method that achieves the best known stochastic first-order oracle complexity by utilizing approximate second-order information. It has been proven to reach an ε-first-order stationary point with improved practical performance. Additionally, quasi-Newton methods, such as L-BFGS, have been proposed for solving large-scale machine learning problems. These methods construct approximate Hessian matrices using gradient information and have shown robust convergence and fast training times in deep learning applications. Decentralized stochastic quasi-Newton methods have also been developed, which achieve faster convergence compared to existing decentralized stochastic first-order algorithms without requiring extra sampling or communication.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| The provided paper discusses a novel faster stochastic quasi-Newton method (SpiderSQN) for machine learning, but it does not provide a literature survey on quasi-Newton methods for machine learning. | |
2 Citations | The provided paper investigates stochastic quasi-Newton methods for decentralized learning, but it does not specifically discuss quasi-Newton methods for machine learning. |
| The provided paper discusses the use of quasi-Newton methods, specifically the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach, for optimization in deep learning applications. It provides convergence analysis and empirical results on image classification tasks and deep reinforcement learning. | |
| The paper provides a literature survey on stochastic quasi-Newton methods for machine learning, focusing on BFGS-based methods and discussing algorithmic improvements and future research directions. | |
12 Citations | The provided paper discusses two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems in machine learning. |
Related Questions
What are the most popular papers in machine learning?5 answersMachine learning research encompasses various domains, with popular papers covering a range of topics. A comprehensive survey on Machine Learning Testing (ML testing) identified 138 papers focusing on testing properties, components, workflow, and application scenarios. In health research, machine learning algorithms like classification and regression trees, least absolute shrinkage and selection operator, and random forest are utilized for understanding risk groups and variable selection in maternal outcomes research. Additionally, the application of machine learning in predicting incident heart failure using electronic health records has gained traction, with a focus on developing predictive models with a larger number of predictors for improved performance. These papers highlight the diverse applications and advancements in machine learning research across different fields.
What is the most review paper about artificial neural network?4 answersThe most reviewed paper about artificial neural networks is the one by Farizawani Ab Ghani, Mohd Rivaie, Marina Yusoff, and Mazidah Puteh. They review and study different types of optimization techniques used in neural network applications, focusing on how optimization algorithms and their modified models have been applied and implemented in neural network training. They also explore how optimization can impact the execution of performance results and training analysis.
What are the best surveys about deep learning?4 answersDeep learning has been the focus of several surveys in different application areas. One notable survey is by Nikolenko et al., which provides an overview of deep learning applications in natural language processing (NLP) tasks such as sentiment analysis, machine translation, and question answering. Another survey by Zhao et al.explores the potential of deep learning in sports performance, discussing algorithms, datasets, and challenges in this domain. Pillonetto et al.present a survey on deep learning from a system identification perspective, covering topics such as model architectures and optimization techniques. Additionally, there is a survey on deep learning techniques for relation extraction (RE) by an anonymous author, which categorizes existing works based on text representation, context encoding, and triplet prediction. These surveys provide valuable insights into the applications and challenges of deep learning in various domains.
What are the hot topics in machine learning?4 answersThe hot topics in machine learning include high dimensional data and the curse of dimensionality, scalability of machine learning algorithms, difficulty of optimization, and bad explicability. Additionally, deep learning, big data, and probabilistic graphical models are also considered hot topics in the field. Machine learning in biomedical literature analysis and text retrieval is another area of focus, with advancements in techniques for analyzing biomedical text being presented in conferences and publications. Furthermore, the top 10 topics in machine learning research, determined through a quantitative analysis of abstracts, encompass models, optimization, data, features, and more. Overall, the hot topics in machine learning revolve around addressing challenges in high dimensional data, scalability, optimization, and explicability, as well as advancements in deep learning, big data, probabilistic graphical models, and biomedical literature analysis.
Significant issues of machine learning theory?5 answersMachine learning theory faces several significant issues. One challenge is the need for additional mechanisms or procedures to predict a large number of new classes while managing privacy. Another issue is the high dimensionality of data, which can complicate the learning process and generate inaccurate results. The vulnerability of machine learning algorithms to manage errors is also a concern. Furthermore, machine learning techniques often lack variability. Incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability is crucial for the acceptance and compliance of ML-based technologies. The interpretability, fairness, safety, and privacy of machine learning models are also important concerns. In the context of education and teaching, machine learning has shown great advantages in prediction and evaluation, as it is not affected by human factors and can build models in complex computing environments.
Is elasticnet considered machine learning method?3 answersYes, elasticnet is considered a machine learning method. It is a regularization algorithm that combines the ℓ1 and ℓ2 norms to perform variable selection and handle correlated variables. It is commonly used for supervised generalized linear model estimation via penalized maximum likelihood. The elasticnet method has been extended to semi-supervised learning with the introduction of the generalized semi-supervised elastic net (s2net). Elasticnet constraints have also been applied to multiple kernel learning (MKL) problems, allowing for efficient optimization of kernel weights. Additionally, the ElasticNET algorithm has been used for accurate and high-resolution reconstruction of tomographic images in electrical tomography. Elastic Federated Learning (EFL) is another application of elasticnet, which addresses heterogeneity in federated learning systems and compresses communications for efficient training.