114,697 research outputs found
Deep Multiple Kernel Learning
Deep learning methods have predominantly been applied to large artificial
neural networks. Despite their state-of-the-art performance, these large
networks typically do not generalize well to datasets with limited sample
sizes. In this paper, we take a different approach by learning multiple layers
of kernels. We combine kernels at each layer and then optimize over an estimate
of the support vector machine leave-one-out error rather than the dual
objective function. Our experiments on a variety of datasets show that each
layer successively increases performance with only a few base kernels.Comment: 4 pages, 1 figure, 1 table, conference pape
Neural Generalization of Multiple Kernel Learning
Multiple Kernel Learning is a conventional way to learn the kernel function
in kernel-based methods. MKL algorithms enhance the performance of kernel
methods. However, these methods have a lower complexity compared to deep
learning models and are inferior to these models in terms of recognition
accuracy. Deep learning models can learn complex functions by applying
nonlinear transformations to data through several layers. In this paper, we
show that a typical MKL algorithm can be interpreted as a one-layer neural
network with linear activation functions. By this interpretation, we propose a
Neural Generalization of Multiple Kernel Learning (NGMKL), which extends the
conventional multiple kernel learning framework to a multi-layer neural network
with nonlinear activation functions. Our experiments on several benchmarks show
that the proposed method improves the complexity of MKL algorithms and leads to
higher recognition accuracy
Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods
Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM) based on a hybrid intelligent algorithms and kernel incremental extreme learning machine is proposed. At first, hybrid intelligent algorithms are proposed based on differential evolution (DE) and multiple population grey wolf optimization (MPGWO) methods which used to optimize the hidden layer neuron parameters and then to determine the effective hidden layer neurons number. The learning efficiency of the algorithm is improved by reducing the network complexity. Then, we bring in the deep network structure to the kernel incremental extreme learning machine to extract the original input data layer by layer gradually. The experiment results show that the HI-DKIELM methods proposed in this paper with more compact network structure have higher prediction accuracy and better ability of generation compared with other ELM methods
FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains,
especially in image and speech recognition. As the backbone of deep learning,
deep neural networks (DNNs) consist of multiple layers of various types with
hundreds to thousands of neurons. Embedded platforms are now becoming essential
for deep learning deployment due to their portability, versatility, and energy
efficiency. The large model size of DNNs, while providing excellent accuracy,
also burdens the embedded platforms with intensive computation and storage.
Researchers have investigated on reducing DNN model size with negligible
accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN
training and inference model suitable for embedded platforms with reduced
asymptotic complexity of both computation and storage, making our approach
distinguished from existing approaches. We develop the training and inference
algorithms based on FFT as the computing kernel and deploy the FFT-based
inference model on embedded platforms achieving extraordinary processing speed.Comment: Design, Automation, and Test in Europe (DATE) For source code, please
contact Mahdi Nazemi at <[email protected]
The Benefit of Multitask Representation Learning
We discuss a general method to learn data representations from multiple
tasks. We provide a justification for this method in both settings of multitask
learning and learning-to-learn. The method is illustrated in detail in the
special case of linear feature learning. Conditions on the theoretical
advantage offered by multitask representation learning over independent task
learning are established. In particular, focusing on the important example of
half-space learning, we derive the regime in which multitask representation
learning is beneficial over independent task learning, as a function of the
sample size, the number of tasks and the intrinsic data dimensionality. Other
potential applications of our results include multitask feature learning in
reproducing kernel Hilbert spaces and multilayer, deep networks.Comment: To appear in Journal of Machine Learning Research (JMLR). 31 page
A theory of representation learning in deep neural networks gives a deep generalisation of kernel methods
The successes of modern deep neural networks (DNNs) are founded on their
ability to transform inputs across multiple layers to build good high-level
representations. It is therefore critical to understand this process of
representation learning. However, we cannot use standard theoretical approaches
involving infinite width limits, as they eliminate representation learning. We
therefore develop a new infinite width limit, the representation learning
limit, that exhibits representation learning mirroring that in finite-width
networks, yet at the same time, remains extremely tractable. For instance, the
representation learning limit gives exactly multivariate Gaussian posteriors in
deep Gaussian processes with a wide range of kernels, including all isotropic
(distance-dependent) kernels. We derive an elegant objective that describes how
each network layer learns representations that interpolate between input and
output. Finally, we use this limit and objective to develop a flexible, deep
generalisation of kernel methods, that we call deep kernel machines (DKMs). We
show that DKMs can be scaled to large datasets using methods inspired by
inducing point methods from the Gaussian process literature, and we show that
DKMs exhibit superior performance to other kernel-based approaches
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