• CiteSeerX — A Framework for Computation-Memory Algorithmic ...

    In this paper, we present a dynamic memory model and associ-ated optimization framework that finds a machine-dependent, near-optimal implementation of an algorithm by exploiting the computation-memory tradeoff. By optimal, we mean an imple-mentation that has the fastest running time given the specification of the machine memory hierarchy.

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  • Practical Bayesian Optimization of Machine Learning Algorithms

    There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP).

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  • Biomedical informatics with optimization and machine ...

    In the paper Machine Learning to Predict Rapid Progression of Carotid Atherosclerosis in Patients with Impaired Glucose Tolerance, Xia Hu, Peter Reaven, Aramesh Saremi, Ninghao Liu, Mohammed Abbasi, Huan Liu, and Raymond Q. Migrino study the important problem of predicting the rapid progression of carotid intima-media thickness in impaired ...

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  • Machine Learning in Communications and Networks | IEEE ...

    This JSAC Series will focus on machine learning solutions to problems in communication networks, across various layers and within a broad range of applications. The topics of interest include, but are not limited to, machine learning, especially deep learning, for signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application ...

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  • GitHub - hibayesian/awesome-automl-papers: A curated list ...

    Awesome-AutoML-Papers. Awesome-AutoML-Papers is a curated list of automated machine learning papers, articles, tutorials, slides and projects.Star this repository, and then you can keep abreast of the latest developments of this booming research field. Thanks to all the people who made contributions to this project. Join us and you are welcome to be a contributor.

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  • Optimization problems for machine learning: A survey ...

    This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching ...

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  • Emulating the Expert: Inverse Optimization through Online ...

    Aug 18, 2020· %0 Conference Paper %T Emulating the Expert: Inverse Optimization through Online Learning %A Andreas Bärmann %A Sebastian Pokutta %A Oskar Schneider %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-barmann17a %I PMLR %J Proceedings of Machine .

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  • [PDF] Optimization Methods for Large-Scale Machine ...

    This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale ...

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  • Introduction to Convex Optimization for Machine Learning

    Duchi (UC Berkeley) Convex Optimization for Machine Learning Fall 2009 23 / 53. Convex Optimization Problems It's nice to be convex Theorem If xˆ is a local minimizer of a convex optimization problem, it is a global minimizer.

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  • [PDF] A Bridging Framework for Model Optimization and Deep ...

    In this work, we provide a new framework, named Propagative Convergent Network (PCN), to bridge the gaps between these two different methodologies (i.e., model optimization and deep propagation) in a collaborative manner. On the one hand, we demonstrate how to utilize PCN as a deeply-trained solver for nonconvex model optimization.

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  • [PDF] RobOptim: an Optimization Framework for Robotics ...

    Numerical optimization is useful for various areas of robotics. However tackling optimization problems properly requires the use of non-trivial algorithms whose tuning is challenging. RobOptim aims at providing a unified framework for different categories of optimization problems while relying on strong C++ typing to ensure efficient and correct computations. This paper presents this software ...

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  • A Framework for Optimizing Paper Matching

    a flexible framework for optimizing paper matching. We investigate approaches that use incomplete information in the form of a limited number of suitability scores: our ba-sic framework predicts missing scores using learning tech-niques and then finds optimal matchings using both ob-served and predicted scores. Within this framework, we

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  • The LambdaLoss Framework for Ranking Metric Optimization

    In this paper, we fill this theoretical gap by proposing Lamb-daLoss, a probabilistic framework for ranking metric optimization. We show that LambdaRank becomes a special configuration in the LambdaLoss framework and a well-defined loss is thus pre-sented for LambdaRank in this paper.

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  • Optimization of Material Composition of Li‐Intercalated ...

    Full Paper. Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns. Hirofumi Hazama. Corresponding Author. E-mail address: [email protected]

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  • A Hybrid Contact Implementation Framework for Finite ...

    May 14, 2020· This paper is devoted to the topology optimization of machine tools with contact constraints. A hybrid approach is followed that combines the computational contact problem framework and an obtained stable contact stiffness function (analytically or experimentally).

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  • New paper: "Risks from learned optimization" - Machine ...

    Jun 07, 2019· Evan Hubinger, Chris van Merwijk, Vladimir Mikulik, Joar Skalse, and Scott Garrabrant have a new paper out: "Risks from learned optimization in advanced machine learning systems." The paper's abstract: We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer—a situation we refer to... Read more »

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  • Multi-Query Optimization in MapReduce Framework

    of jobs given the expanded repertoire of optimization tech-niques. Section 6 presents a performance evaluation of the presented techniques. Section 7 presents related work, and we conclude in Section 8. 2. BACKGROUND In this section, we review the MapReduce framework and introduce the assumptions and notations used in the paper. 2.1 MapReduce ...

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  • Markov Decision Process (MDP) Framework for Software .

    the user specified time T. Parameter T, which is assumed to be given in this paper, is user-profile driven and can be estimated with good accuracy as shown in [17]. In summary, the key contributions of this paper are: 1. A methodology for dynamic power optimization of applications to prolong the

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  • Practical Bayesian Optimization of Machine Learning Algorithms

    Practical Bayesian Optimization of Machine Learning Algorithms The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Snoek, Jasper, Hugo Larochelle, and Ryan Prescott Adams. 2012. Practical Bayesian optimization of machine learning algorithms.

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  • Optimization Online - Finite-Sample Guarantees for ...

    Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality. Rui Gao(rui.gao mccombs.utexas.edu). Abstract: Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance.Despite its recent empirical success in operations research and ...

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  • A Unified Algorithmic Framework for Block-Structured ...

    Dec 25, 2015· A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing Abstract: This article presents a powerful algorithmic framework for big data optimization, called the .

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  • 2020 DAC Accepted Papers | Design Automation Conference

    Exploration of Design Space and Runtime Optimization for Affective Computing in Machine Learning Empowered Ultra-low Power SoC: 295-1299: Exploring a Bayesian Optimization Framework Compatible with Digital Standard Flow for Soft-Error-Tolerant Circuit: 295-1291: Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection: 295-1806

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  • Artificial Intelligence and Machine Learning in Software ...

    On April 2, 2019, the FDA published a discussion paper "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device ...

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  • A general framework of workpiece setup optimization for ...

    Request PDF | A general framework of workpiece setup optimization for the five-axis machining | In five-axis machining, the setup of workpiece on the machine's table is an essential element of the ...

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  • Artificial Intelligence and Machine Learning in Software ...

    On April 2, 2019, the FDA published a discussion paper "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device ...

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  • A Hybrid Intelligent System Framework for the Prediction ...

    The remaining parts of the paper are structured as follows: in Section 2, the background information regarding heart disease dataset briefly reviews the theoretical and mathematical background of feature selection and classification algorithms of machine learning. It additionally discusses cross-validation method and performance evaluation metrics.

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  • Practical Bayesian Optimization of Machine Learning Algorithms

    There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP).

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  • LLVM: A Compilation Framework for Lifelong Program ...

    Jan 30, 2004· The rest of this paper is organized as follows. Section 2 describes the LLVM code representation. Section 3 then describes the design of the LLVM compiler framework. Sec-tion 4 discusses our evaluation of the LLVM system as de-scribed above. Section 5 compares LLVM with related pre-vious systems. Section 6 concludes with a summary of the paper. 2.

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  • Machine Learning Optimization Algorithms & Portfolio ...

    Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal from a financial point of view (Michaud, 1989).

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  • Optimization of Process Parameters for Powder Bed Fusion ...

    Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Machine Learning is based on big amounts of data. This paper provides a conceptual framework ...

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  • Machine Learning and Optimization - NYU Courant

    Machine Learning and Optimization Andres Munoz Courant Institute of Mathematical Sciences, New York, NY. Abstract. This nal project attempts to show the di erences of ma-chine learning and optimization. In particular while optimization is con-cerned with exact solutions machine learning is concerned with general-ization abilities of learners.

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