9,820 research outputs found
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
In this paper we propose and investigate a novel nonlinear unit, called
unit, for deep neural networks. The proposed unit receives signals from
several projections of a subset of units in the layer below and computes a
normalized norm. We notice two interesting interpretations of the
unit. First, the proposed unit can be understood as a generalization of a
number of conventional pooling operators such as average, root-mean-square and
max pooling widely used in, for instance, convolutional neural networks (CNN),
HMAX models and neocognitrons. Furthermore, the unit is, to a certain
degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013)
which achieved the state-of-the-art object recognition results on a number of
benchmark datasets. Secondly, we provide a geometrical interpretation of the
activation function based on which we argue that the unit is more
efficient at representing complex, nonlinear separating boundaries. Each
unit defines a superelliptic boundary, with its exact shape defined by the
order . We claim that this makes it possible to model arbitrarily shaped,
curved boundaries more efficiently by combining a few units of different
orders. This insight justifies the need for learning different orders for each
unit in the model. We empirically evaluate the proposed units on a number
of datasets and show that multilayer perceptrons (MLP) consisting of the
units achieve the state-of-the-art results on a number of benchmark datasets.
Furthermore, we evaluate the proposed unit on the recently proposed deep
recurrent neural networks (RNN).Comment: ECML/PKDD 201
New pixel-DCT domain coding technique for object based and frame based prediction error
2004-2005 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
Cosine-Based Clustering Algorithm Approach
Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods
Customized television: Standards compliant advanced digital television
This correspondence describes a European Union supported collaborative project called CustomTV based on the premise that future TV sets will provide all sorts of multimedia information and interactivity, as well as manage all such services according to each userâs or group of userâs preferences/profiles. We have demonstrated the potential of recent standards (MPEG-4 and MPEG-7) to implement such a scenario by building
the following services: an advanced EPG, Weather Forecasting, and Stock Exchange/Flight Information
Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells
We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA laye
MPEG-4 Software Video Encoding
A Thesis submitted in fulfillment of the requirements of the degree of doctor of Philosophy in the University of LondonThis thesis presents a software model that allows a parallel decomposition of the
MPEG-4 video encoder onto shared memory architectures, in order to reduce its
total video encoding time.
Since a video sequence consists of video objects each of which is likely to have
different encoding requirements, the model incorporates a scheduler which
(a) always selects the most appropriate video object for encoding and,
(b) employs a mechanism for dynamically allocating video objects allocation onto
the system processors, based on video object size information.
Further spatial video object parallelism is exploited by applying the single program
multiple data (SPMD) paradigm within the different modules of the MPEG-4
video encoder. Due to the fact that not all macroblocks have the same processing
requirements, the model also introduces a data partition scheme that generates tiles
with identical processing requirements. Since, macroblock data dependencies
preclude data parallelism at the shape encoder the model also introduces a new
mechanism that allows parallelism using a circular pipeline macroblock technique
The encoding time depends partly on an encoderâs computational complexity. This
thesis also addresses the problem of the motion estimation, as its complexity has a
significant impact on the encoderâs complexity. In particular, two fast motion
estimation algorithms have been developed for the model which reduce the
computational complexity significantly. The thesis includes experimental results on a four processor shared memory
platform, Origin200
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