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Large scale distributed neural network training through online distillation
[article]
2020
arXiv
pre-print
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Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. ...
Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. ...
Another important comparison is to an ensemble of two neural networks, each trained with 128 GPUs and synchronous SGD. ...
arXiv:1804.03235v2
fatcat:ftkf2wofpjhqxaeilzksiwpfsq
Fourier-Based Parametrization of Convolutional Neural Networks for Robust Time Series Forecasting
[chapter]
2019
Lecture Notes in Computer Science
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To that end, we use Convolutional Neural Networks (CNNs) for time series forecasting and determine a part of the network layout based on the time series' Fourier coefficients. ...
Instead of optimizing hyperparameters by training multiple models, we propose a method to estimate optimal hyperparameters directly from the characteristics of the time series at hand. ...
The neural networks were trained on a NVIDIA Tesla K80 GPU and an Intel i7-6820HQ CPU was used for all other models as these don't profit from GPU usage. ...
doi:10.1007/978-3-030-33778-0_39
fatcat:mepeyuimnbdbtdec2vyjzjlbpy
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
[article]
2020
arXiv
pre-print
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We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. ...
We give theoretical justifications of the proposed idea and validate it on a large dataset. ...
We want to express our sincere gratitude towards Aurelian Marcu and The Center for Advanced Laser Technologies (CETAL) for their generosity and providing us access to GPU computational resources. ...
arXiv:2010.01086v2
fatcat:ng27p5utdnabplogh5qhokdlh4
Learning Neural Network Subspaces
[article]
2021
arXiv
pre-print
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With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. ...
These neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance of independently trained networks without the training cost. ...
We acknowledge Ludwig Schmidt for correcting Definitions 1 and 2 which previously measured average accuracy instead of worst case. MW acknowledges Apple for providing internship support. ...
arXiv:2102.10472v3
fatcat:bl3y3xhzrzgx5a7s5n4eqmwsfa
Randomized Prior Functions for Deep Reinforcement Learning
[article]
2018
arXiv
pre-print
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There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorly-suited to sequential decision problems. ...
We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable 'prior' network to each ensemble member. ...
This paper can be thought of as a specific type of 'deep exploration via randomized value functions', whose line of research has been crucially driven by the contributions of (and conversations with) Benjamin ...
arXiv:1806.03335v2
fatcat:zkly3q224zad5cpqk7esoazr3e
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
[article]
2020
arXiv
pre-print
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As a baseline, we used an ensemble of neural networks that only use ReLU activations. We tested our networks on several small and medium sized biomedical image datasets. ...
In this paper we classify biomedical images using ensembles of neural networks. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
arXiv:2011.11834v1
fatcat:zslgtqwllzgzbeuqpurmnrhe4e
An Approach to Performance Prediction for Parallel Applications
[chapter]
2005
Lecture Notes in Computer Science
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In contrast, we employ multilayer neural networks trained on input data from executions on the target platform. ...
Our model predicts performance on two large-scale parallel platforms within 5%-7% error across a large, multidimensional parameter space. ...
We apply bagging to train an ensemble of models from the dataset, averaging predictions from the ensemble to reduce model variance. ...
doi:10.1007/11549468_24
fatcat:c7idlttjhvdjbkx6mo2ezw5mpq
Behavioural Intrusion Detection in Water Distribution Systems Using Neural Networks
2020
IEEE Access
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Figure 10 shows the results of the implemented neural network architectures compared to the machine learning algorithms proposed in BATADAL implemented on the test dataset. ...
In this paper a number of neural network architectures where trained on the normal BATADAL dataset. ...
doi:10.1109/access.2020.3032251
fatcat:kv6rzbcge5fn7i3f7fnqzzsc7q
A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems
2021
Cluster Computing
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The results of this investigation show the satisfying detection performance of the proposed stacked deep learning approach. ...
Specifically, we investigate the feasibility of a deep learning approach for intrusion detection in SCADA systems. ...
The proposed stacked deep learning-driven method To discriminate cyber attacks from normal operations, we propose a stacked deep learning model that ensembles the results of five forward neural networks ...
doi:10.1007/s10586-021-03426-w
pmid:34629940
pmcid:PMC8490144
fatcat:wgvodkfndbc2xbb5dr2y3wtgam
Neural ensemble decoding for topological quantum error-correcting codes
[article]
2019
arXiv
pre-print
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We apply our framework to an ensemble of Minimum-Weight Perfect Matching (MWPM) and Hard-Decision Re-normalization Group (HDRG) decoders for the surface code in the depolarizing noise model. ...
We use machine learning techniques to assign a given error syndrome to the decoder which is likely to decode it correctly. ...
ACKNOWLEDGMENTS We thank Pooya Ronagh for useful discussions regarding methods for improving the training of the machine learning model. ...
arXiv:1905.02345v1
fatcat:zrsfz4k4wbhydodcfc4aslt3iy
The Curious Case of Convex Neural Networks
[article]
2021
arXiv
pre-print
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We demonstrate the efficacy of the proposed idea using thorough experiments and ablation studies on standard image classification datasets with three different neural network architectures. ...
In this paper, we investigate a constrained formulation of neural networks where the output is a convex function of the input. ...
Even while training, IOC-NNs show no signs of fitting on noisy data and efficiently learns patterns from non noisy data. ...
arXiv:2006.05103v3
fatcat:4wei3xyytzcjpeu4yvuthpebhu
A Survey on Distributed Machine Learning
2020
ACM Computing Surveys
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Chen et al. [32] developed DianNao, a hardware accelerator for large-scale neural networks with a small area footprint. ...
The experimental evaluation using the different layers of several large neural network structures [48, 70, 90, 132, 133] shows a performance speedup of three orders of magnitude and an energy reduction ...
Because neural networks require a large number of nodes, the understandability of a neural network's thought process is lower compared to, e.g., decision trees. ...
doi:10.1145/3377454
fatcat:apwpdtza4zc2tcn37hnxxrb74u
Learning for Robust Combinatorial Optimization: Algorithm and Application
[article]
2021
arXiv
pre-print
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Learning to optimize (L2O) has recently emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks and offering lower runtime complexity ...
While L2O has been applied to various problems, a crucial yet challenging class of problems -- robust combinatorial optimization in the form of minimax optimization -- have largely remained under-explored ...
Then, with training samples and the loss
which includes an ensemble of neural networks to efficiently function in Eqn (2), any standard learning approaches, like
produce a solution to the inner maximization ...
arXiv:2112.10377v1
fatcat:yvzecurgqzd4nc7gfuastvc6fm
Selectivity estimation for range predicates using lightweight models
2019
Proceedings of the VLDB Endowment
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We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. ...
While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. ...
Our study includes neural networks and tree-based ensembles. ...
doi:10.14778/3329772.3329780
fatcat:tfd3rj5zcfavpnxq2wxqu4akmq
Mapping neutron star data to the equation of state using the deep neural network
[article]
2019
arXiv
pre-print
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Here we show results from a novel theoretical technique to utilize deep neural network with supervised learning. ...
We input up-to-date observational data from neutron star X-ray radiations into the trained neural network and estimate a relation between the pressure and the mass density. ...
For the neural network to learn the correlation between the variances (σ R i , σ M i ) and how far the actual data is off from the genuine M -R curve, we prepare 100 ensembles of different variances for ...
arXiv:1903.03400v2
fatcat:ad7y7x7mozd2nkqwdth3sy4egy
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