2,147 research outputs found
Pareto-Path Multi-Task Multiple Kernel Learning
A traditional and intuitively appealing Multi-Task Multiple Kernel Learning
(MT-MKL) method is to optimize the sum (thus, the average) of objective
functions with (partially) shared kernel function, which allows information
sharing amongst tasks. We point out that the obtained solution corresponds to a
single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO)
problem, which considers the concurrent optimization of all task objectives
involved in the Multi-Task Learning (MTL) problem. Motivated by this last
observation and arguing that the former approach is heuristic, we propose a
novel Support Vector Machine (SVM) MT-MKL framework, that considers an
implicitly-defined set of conic combinations of task objectives. We show that
solving our framework produces solutions along a path on the aforementioned PF
and that it subsumes the optimization of the average of objective functions as
a special case. Using algorithms we derived, we demonstrate through a series of
experimental results that the framework is capable of achieving better
classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Evolutionary Construction of Convolutional Neural Networks
Neuro-Evolution is a field of study that has recently gained significantly
increased traction in the deep learning community. It combines deep neural
networks and evolutionary algorithms to improve and/or automate the
construction of neural networks. Recent Neuro-Evolution approaches have shown
promising results, rivaling hand-crafted neural networks in terms of accuracy.
A two-step approach is introduced where a convolutional autoencoder is created
that efficiently compresses the input data in the first step, and a
convolutional neural network is created to classify the compressed data in the
second step. The creation of networks in both steps is guided by by an
evolutionary process, where new networks are constantly being generated by
mutating members of a collection of existing networks. Additionally, a method
is introduced that considers the trade-off between compression and information
loss of different convolutional autoencoders. This is used to select the
optimal convolutional autoencoder from among those evolved to compress the data
for the second step. The complete framework is implemented, tested on the
popular CIFAR-10 data set, and the results are discussed. Finally, a number of
possible directions for future work with this particular framework in mind are
considered, including opportunities to improve its efficiency and its
application in particular areas
EvoSplit: An evolutionary approach to split a multi-label data set into disjoint subsets
This paper presents a new evolutionary approach, EvoSplit, for the
distribution of multi-label data sets into disjoint subsets for supervised
machine learning. Currently, data set providers either divide a data set
randomly or using iterative stratification, a method that aims to maintain the
label (or label pair) distribution of the original data set into the different
subsets. Following the same aim, this paper first introduces a single-objective
evolutionary approach that tries to obtain a split that maximizes the
similarity between those distributions independently. Second, a new
multi-objective evolutionary algorithm is presented to maximize the similarity
considering simultaneously both distributions (labels and label pairs). Both
approaches are validated using well-known multi-label data sets as well as
large image data sets currently used in computer vision and machine learning
applications. EvoSplit improves the splitting of a data set in comparison to
the iterative stratification following different measures: Label Distribution,
Label Pair Distribution, Examples Distribution, folds and fold-label pairs with
zero positive examples
Knowledge Distillation for Multi-task Learning
Multi-task learning (MTL) is to learn one single model that performs multiple
tasks for achieving good performance on all tasks and lower cost on
computation. Learning such a model requires to jointly optimize losses of a set
of tasks with different difficulty levels, magnitudes, and characteristics
(e.g. cross-entropy, Euclidean loss), leading to the imbalance problem in
multi-task learning. To address the imbalance problem, we propose a knowledge
distillation based method in this work. We first learn a task-specific model
for each task. We then learn the multi-task model for minimizing task-specific
loss and for producing the same feature with task-specific models. As the
task-specific network encodes different features, we introduce small
task-specific adaptors to project multi-task features to the task-specific
features. In this way, the adaptors align the task-specific feature and the
multi-task feature, which enables a balanced parameter sharing across tasks.
Extensive experimental results demonstrate that our method can optimize a
multi-task learning model in a more balanced way and achieve better overall
performance.Comment: We propose a knowledge distillation method for addressing the
imbalance problem in multi-task learnin
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