Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Automatic Model Selection for Neural Networks
Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-…
Distributed ComputingEfficient Neural NetworkmodelModel SelectionDeep Learning Acceleration Techniques for Real Time Mobile Vision Applications
Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such …
Deep LearningDistributed Computingobject-detectionObject DetectionFast communication-efficient spectral clustering over distributed data
The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already i…
ClusteringDistributed ComputingDANA: Scalable Out-of-the-box Distributed ASGD Without Retuning
Distributed computing can significantly reduce the training time of neural networks. Despite its potential, however, distributed training has not been widely adopted: scaling the training process is difficult, and existi…
Distributed ComputingAutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool prov…
Distributed ComputingWONDER: Weighted one-shot distributed ridge regression in high dimensions
In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we …
Distributed ComputingregressionUnityVocal Bursts Intensity PredictionEnergy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce
In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks. Using the Map-Reduce distributed computing framework, the tasks are optimally distributed amongs…
Distributed ComputingGradient Coding with Clustering and Multi-message Communication
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion. For problems with massive datasets, computations are distributed to many p…
ClusteringDistributed ComputingDevelopment details and computational benchmarking of DEPAM
In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current …
BenchmarkingDistributed ComputingVector operations for accelerating expensive Bayesian computations -- a tutorial guide
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distribute…
Distributed ComputingEvolutionary Neural AutoML for Deep Learning
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a …
AutoMLDeep LearningDistributed ComputingEvolutionary Algorithms+5Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample
We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning. Contrary to the classical variants of these methods that seque…
BenchmarkingBIG-bench Machine LearningBinary ClassificationDistributed Computing+1Distributed Learning with Compressed Gradient Differences
Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods based on the compression (e.g., sparsificat…
Distributed ComputingQuantizationDINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
For optimization of a sum of functions in a distributed computing environment, we present a novel communication efficient Newton-type algorithm that enjoys a variety of advantages over similar existing methods. Similar t…
Distributed ComputingVocal Bursts Type PredictionDistance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition
Distributed computing which uses Web services as fundamental elements, enables high-speed development of software applications through composing many interoperating, distributed, re-usable, and autonomous services. As a …
Distributed ComputingService CompositionReal-time cortical simulations: energy and interconnect scaling on distributed systems
We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on distributed computing platforms. Also, the s…
Distributed ComputingCommunication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning
This paper deals with distributed policy optimization in reinforcement learning, which involves a central controller and a group of learners. In particular, two typical settings encountered in several applications are co…
Distributed ComputingMulti-agent Reinforcement LearningPolicy Gradient Methodsreinforcement-learning+2DLHub: Model and Data Serving for Science
While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to sup…
Distributed ComputingmodelPartitioned Variational Inference: A unified framework encompassing federated and continual learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a bewildering array of algorithmic option…
Continual LearningDistributed ComputingFederated LearningVariational InferenceDistributed Gradient Descent with Coded Partial Gradient Computations
Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out …
Distributed Computing