10,447 research outputs found
ART Neural Networks: Distributed Coding and ARTMAP Applications
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
A Neural Model of How the Brain Represents and Compares Multi-Digit Numbers: Spatial and Categorical Processes
Both animals and humans are capable of representing and comparing numerical quantities, but only humans seem to have evolved multi-digit place-value number systems. This article develops a neural model, called the Spatial Number Network, or SpaN model, which predicts how these shared numerical capabilities are computed using a spatial representation of number quantities in the Where cortical processing stream, notably the Inferior Parietal Cortex. Multi-digit numerical representations that obey a place-value principle are proposed to arise through learned interactions between categorical language representations in the What cortical processing stream and the Where spatial representation. It is proposed that learned semantic categories that symbolize separate digits, as well as place markers like "tens," "hundreds," "thousands," etc., are associated through learning with the corresponding spatial locations of the Where representation, leading to a place-value number system as an emergent property of What-Where information fusion. The model quantitatively simulates error rates in quantification and numerical comparison tasks, and reaction times for number priming and numerical assessment and comparison tasks. In the Where cortical process, it is proposed that transient responses to inputs are integrated before they activate an ordered spatial map that selectively responds to the number of events in a sequence. Neural mechanisms are defined which give rise to an ordered spatial numerical map ordering and Weber law characteristics as emergent properties. The dynamics of numerical comparison are encoded in activity pattern changes within this spatial map. Such changes cause a "directional comparison wave" whose properties mimic data about numerical comparison. These model mechanisms are variants of neural mechanisms that have elsewhere been used to explain data about motion perception, attention shifts, and target tracking. Thus, the present model suggests how numerical representations may have emerged as specializations of more primitive mechanisms in the cortical Where processing stream. The model's What-Where interactions can explain human psychophysical data, such as error rates and reaction times, about multi-digit (base 10) numerical stimuli, and describe how such a competence can develop through learning. The SpaN model and its explanatory range arc compared with other models of numerical representation.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333
Reducing Spatial Data Complexity for Classification Models
Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly
increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy
corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be
frequently retrained which fiirther hinders their use. Various data reduction techniques ranging from data sampling up to
density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do
not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our
response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled
spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we
demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are
moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled
by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of
the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions.
As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with
the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced
dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments
if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of
classification performance at the comparable compression levels
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
A Neural Architecture for Designing Truthful and Efficient Auctions
Auctions are protocols to allocate goods to buyers who have preferences over
them, and collect payments in return. Economists have invested significant
effort in designing auction rules that result in allocations of the goods that
are desirable for the group as a whole. However, for settings where
participants' valuations of the items on sale are their private information,
the rules of the auction must deter buyers from misreporting their preferences,
so as to maximize their own utility, since misreported preferences hinder the
ability for the auctioneer to allocate goods to those who want them most.
Manual auction design has yielded excellent mechanisms for specific settings,
but requires significant effort when tackling new domains. We propose a deep
learning based approach to automatically design auctions in a wide variety of
domains, shifting the design work from human to machine. We assume that
participants' valuations for the items for sale are independently sampled from
an unknown but fixed distribution. Our system receives a data-set consisting of
such valuation samples, and outputs an auction rule encoding the desired
incentive structure. We focus on producing truthful and efficient auctions that
minimize the economic burden on participants. We evaluate the auctions designed
by our framework on well-studied domains, such as multi-unit and combinatorial
auctions, showing that they outperform known auction designs in terms of the
economic burden placed on participants
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
Continual Local Training for Better Initialization of Federated Models
Federated learning (FL) refers to the learning paradigm that trains machine
learning models directly in the decentralized systems consisting of smart edge
devices without transmitting the raw data, which avoids the heavy communication
costs and privacy concerns. Given the typical heterogeneous data distributions
in such situations, the popular FL algorithm \emph{Federated Averaging}
(FedAvg) suffers from weight divergence and thus cannot achieve a competitive
performance for the global model (denoted as the \emph{initial performance} in
FL) compared to centralized methods. In this paper, we propose the local
continual training strategy to address this problem. Importance weights are
evaluated on a small proxy dataset on the central server and then used to
constrain the local training. With this additional term, we alleviate the
weight divergence and continually integrate the knowledge on different local
clients into the global model, which ensures a better generalization ability.
Experiments on various FL settings demonstrate that our method significantly
improves the initial performance of federated models with few extra
communication costs.Comment: This paper has been accepted to 2020 IEEE International Conference on
Image Processing (ICIP 2020
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