7,442 research outputs found
Masking: A New Perspective of Noisy Supervision
It is important to learn various types of classifiers given training data
with noisy labels. Noisy labels, in the most popular noise model hitherto, are
corrupted from ground-truth labels by an unknown noise transition matrix. Thus,
by estimating this matrix, classifiers can escape from overfitting those noisy
labels. However, such estimation is practically difficult, due to either the
indirect nature of two-step approaches, or not big enough data to afford
end-to-end approaches. In this paper, we propose a human-assisted approach
called Masking that conveys human cognition of invalid class transitions and
naturally speculates the structure of the noise transition matrix. To this end,
we derive a structure-aware probabilistic model incorporating a structure
prior, and solve the challenges from structure extraction and structure
alignment. Thanks to Masking, we only estimate unmasked noise transition
probabilities and the burden of estimation is tremendously reduced. We conduct
extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as
well as the industrial-level Clothing1M with agnostic noise structure, and the
results show that Masking can improve the robustness of classifiers
significantly.Comment: NIPS 2018 camera-ready versio
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We present PyCARL, a PyNN-based common Python programming interface for
hardware-software co-simulation of spiking neural network (SNN). Through
PyCARL, we make the following two key contributions. First, we provide an
interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and
biophysically-detailed SNN simulator. PyCARL facilitates joint development of
machine learning models and code sharing between CARLsim and PyNN users,
promoting an integrated and larger neuromorphic community. Second, we integrate
cycle-accurate models of state-of-the-art neuromorphic hardware such as
TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies
that delay spikes between communicating neurons and degrade performance. PyCARL
allows users to analyze and optimize the performance difference between
software-only simulation and hardware-software co-simulation of their machine
learning models. We show that system designers can also use PyCARL to perform
design-space exploration early in the product development stage, facilitating
faster time-to-deployment of neuromorphic products. We evaluate the memory
usage and simulation time of PyCARL using functionality tests, synthetic SNNs,
and realistic applications. Our results demonstrate that for large SNNs, PyCARL
does not lead to any significant overhead compared to CARLsim. We also use
PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and
demonstrate a significant performance deviation from software-only simulations.
PyCARL allows to evaluate and minimize such differences early during model
development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint
Conference on Neural Networks (IJCNN) 202
Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting
In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and
AI (GCA'18
Effort estimation for object-oriented system using artificial intelligence techniques
Software effort estimation is a vital task in software engineering. The importance of effort estimation becomes critical during early stage of the software life cycle when the details of the software have not been revealed yet. The effort involved in developing a software product plays an important role in determining the success or failure. With the proliferation of software projects and the heterogeneity in their genre, there is a need for efficient effort estimation techniques to enable the project managers to perform proper planning of the Software Life Cycle activates. In the context of developing software using object-oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. There are basically some points approach, which are available for software effort estimation such as Function Point, Use Case Point, Class Point, Object Point, etc. In this thesis, the main goal is to estimate the effort of various software projects using Class Point Approach. The parameters are optimized using various artificial intelligence (AI) techniques such as Multi-Layer Perceptron (MLP), K-Nearest Neighbor Regression (KNN) and Radial Basis Function Network(RBFN), fuzzy logic with various clustering algorithms such as the Fuzzy C-means (FCM) algorithm, K-means clustering algorithm and Subtractive Clustering (SC) algorithm, such as to achieve better accuracy. Furthermore, a comparative analysis of software effort estimation using these various AI techniques has been provided. By estimating the software projects accurately, we can have software with acceptable quality within budget and on planned schedules
Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods
The job of software effort estimation is a critical one in the early stages
of the software development life cycle when the details of requirements are
usually not clearly identified. Various optimization techniques help in
improving the accuracy of effort estimation. The Support Vector Regression
(SVR) is one of several different soft-computing techniques that help in
getting optimal estimated values. The idea of SVR is based upon the computation
of a linear regression function in a high dimensional feature space where the
input data are mapped via a nonlinear function. Further, the SVR kernel methods
can be applied in transforming the input data and then based on these
transformations, an optimal boundary between the possible outputs can be
obtained. The main objective of the research work carried out in this paper is
to estimate the software effort using use case point approach. The use case
point approach relies on the use case diagram to estimate the size and effort
of software projects. Then, an attempt has been made to optimize the results
obtained from use case point analysis using various SVR kernel methods to
achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information
Processing (IJIP
Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid
This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. Different structures of NARMA (Non-linear ARMA) models have been studied. The experimental results have allowed to carry out a comparison between the different neural approaches and a first-principles model. The best neural results are obtained using a parallel model structure based on a recurrent neural network architecture, which guarantees better dynamic approximations than currently employed neural models. The results suggest that parallel models built up with recurrent networks can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits which change from batch installation to installation.Publicad
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
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