37,429 research outputs found
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.Comment: Presented at CVPR 201
Hyperspectral imaging applied to end-of-life (EOL) concrete recycling
The recovery of materials from DW is an important target of the recycling industry and it is important to know which materials are presents in order to set up efficient sorting and/or quality control actions. The implementation of an automatic recognition system of recovered products from End-Of-Life (EOL) concrete materials can be an useful way to maximize DW conversion into secondary raw materials. In this paper a new approach, based on HyperSpectral Imaging (HSI) sensors, is investigated in order to develop suitable and low cost strategies finalized to the preliminary detection and characterization of materials constituting Demolition Waste (DW) flow stream. The described HSI quality control approach is based on the utilization of a device working in the near infrared range (1000-1700 nm). Acquired hyperspectral images were analyzed. Different chemometric methods were applied. Results showed that it is possible to recognize DW materials and to distinguish the recycled aggregates from the investigated contaminants (brick, gypsum, plastic, wood and foam)
A virtual workspace for hybrid multidimensional scaling algorithms
In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
While the use of bottom-up local operators in convolutional neural networks
(CNNs) matches well some of the statistics of natural images, it may also
prevent such models from capturing contextual long-range feature interactions.
In this work, we propose a simple, lightweight approach for better context
exploitation in CNNs. We do so by introducing a pair of operators: gather,
which efficiently aggregates feature responses from a large spatial extent, and
excite, which redistributes the pooled information to local features. The
operators are cheap, both in terms of number of added parameters and
computational complexity, and can be integrated directly in existing
architectures to improve their performance. Experiments on several datasets
show that gather-excite can bring benefits comparable to increasing the depth
of a CNN at a fraction of the cost. For example, we find ResNet-50 with
gather-excite operators is able to outperform its 101-layer counterpart on
ImageNet with no additional learnable parameters. We also propose a parametric
gather-excite operator pair which yields further performance gains, relate it
to the recently-introduced Squeeze-and-Excitation Networks, and analyse the
effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201
A visual workspace for constructing hybrid MDS algorithms and coordinating multiple views
Data can be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualisation. This has led to an abundance of often disparate algorithmic techniques. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This paper presents a system and framework in which a user can easily explore algorithms as well as their hybrid conjunctions and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views of algorithmic and visualisation components. We propose that our approach has two main benefits: significant improvements in run times of MDS algorithms can be achieved, and intermediate views of the data and the visualisation program structure can provide greater insight and control over the visualisation process
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