Constraint (computer-aided design)
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Communication-Efficient Learning of Deep Networks from Decentralized Data Open
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image …
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Improved Training of Wasserstein GANs Open
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only …
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Accurate and Numerically Efficient r<sup>2</sup>SCAN Meta-Generalized Gradient Approximation Open
The recently proposed rSCAN functional [ J. Chem. Phys. 2019 150, 161101] is a regularized form of the SCAN functional [ Phys. Rev. Lett. 2015 115, 036402] that improves SCAN's numerical performance at the expense of breaking constraints k…
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Federated Learning of Deep Networks using Model Averaging Open
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image …
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Review of Causal Discovery Methods Based on Graphical Models Open
A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are diffi…
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Learning-Based Model Predictive Control: Toward Safe Learning in Control Open
Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Mo…
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FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation Open
Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a h…
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Uncertainty and stress: Why it causes diseases and how it is mastered by the brain Open
The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely…
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Optimal sound-absorbing structures Open
Absorption by design, with minimal sample thickness allowed by the law of nature, can now be realized by using a design recipe that incorporates the causal constraint of acoustic response as a crucial element.
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Unsupervised Feature Selection Using Nonnegative Spectral Analysis Open
In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the…
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Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders Open
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-re…
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Review of Visual Saliency Detection With Comprehensive Information Open
Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-i…
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Do online social media cut through the constraints that limit the size of offline social networks? Open
The social brain hypothesis has suggested that natural social network sizes may have a characteristic size in humans. This is determined in part by cognitive constraints and in part by the time costs of servicing relationships. Online soci…
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Object-Part Attention Model for Fine-Grained Image Classification Open
Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to large variance in the same subcateg…
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Regional missense constraint improves variant deleteriousness prediction Open
Given increasing numbers of patients who are undergoing exome or genome sequencing, it is critical to establish tools and methods to interpret the impact of genetic variation. While the ability to predict deleteriousness for any given vari…
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Drivers and barriers to circular economy implementation Open
Purpose Circular economy (CE) has gained considerable attention from researchers and practitioners over the past few years because of its potential social and environmental benefits. However, limited attention has been given in the literat…
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Constraint Satisfaction Open
Search can be made easier in cases where the solution instead of corresponding to an optimal path, is only required to satisfy local consistency conditions. We call such problems Constraint Satisfaction (CS) Problems. For example, in a cro…
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A Survey on Evolutionary Constrained Multiobjective Optimization Open
Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrain…
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A genome-wide mutational constraint map quantified from variation in 76,156 human genomes Open
The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts to assess constraint for non-protein-coding regions …
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From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application Open
The promise of randomized controlled trials is that evidence gathered through the evaluation of a specific program helps us—possibly after several rounds of fine-tuning and multiple replications in different contexts—to inform policy. Howe…
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Access to finance and firm performance: Evidence from African countries Open
This study conducts an empirical investigation of the effects of access to finance on the growth of firms in African countries. In order to achieve this, we made use of a new rich enterprise-level data set from the World Bank's Enterprise …
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On Fairness and Calibration Open
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on what it means for a classification procedure to be "fair." I…
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Constraint on the maximum mass of neutron stars using GW170817 event Open
We revisit the constraint on the maximum mass of cold spherical neutron stars coming from the observational results of GW170817. We develop a new framework for the analysis by employing both energy and angular momentum conservation laws as…
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Machine and deep learning meet genome-scale metabolic modeling Open
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and …
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Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses Open
Weighted model counting, that is, counting the weighted number of satisfying assignments of a propositional formula, is an important tool in probabilistic reasoning. Recently, the use of projected weighted model counting (PWMC) has been pr…
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Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning Open
In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with…
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Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks Open
The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. App…
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Safe Exploration in Continuous Action Spaces Open
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated. We show how to exploit the typically smooth dynamic…
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Contrastive Learning for Cold-Start Recommendation Open
Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative s…
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Mobile traffic forecasting for maximizing 5G network slicing resource utilization Open
The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommod…