40,946 research outputs found
Recommended from our members
Knowledge Management for Public Administrations: Technical Realizations of an Enterprise Attention Management System
The improvement of governments’ efficiency has gained great importance and validity especially in the current times of economic downturn. E-Government constitutes the most contemporary techno-managerial proposition in the track of possible interventions. The paper addresses, more specifically, empowerments necessitated by Public Administration (PA) organizations. Anchored on the needs of three real-life cases, the paper describes the conception and the realization of an IT artefact together with its methodological appeals aiming at improving information access and delivery and thus PAs’ decision making capacity. Our proposition constitutes a novel approach for managing users’ attention in knowledge intensive organizations which goes beyond informing a user about changes in relevant information towards proactively supporting the user to react on changes. The approach is based on an expressive attention model, which is realized by combining ECA (Event-Condition-Action) rules with ontologies. The technical realizations described in the paper constitute the underlying infrastructure of an Enterprise Attention Management System
Linking Visual Development and Learning to Information Processing: Preattentive and Attentive Brain Dynamics
National Science Foundation (SBE-0354378); Office of Naval Research (N00014-95-1-0657
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
The Laminar Architecture of Visual Cortex and Image Processing Technology
The mammalian neocortex is organized into layers which include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article summarizes a model, called the LAMINART model, of how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex stably grows and tunes its circuits to match environmental constraints. Such Laminar Computing completes perceptual groupings that realize the property of Analog Coherence, whereby winning groupings bind together their inducing features without losing their ability to represent analog values of these features. Laminar Computing also efficiently unifies the computational requirements of preattentive filtering and grouping with those of attentional selection. It hereby shows how Adaptive Resonance Theory (ART) principles may be realized within the laminar circuits of neocortex. Applications include boundary segmentation and surface filling-in algorithms for processing Synthetic Aperture Radar images.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-95-1-0657
From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously
Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the
information that they receive from the world in real time
because it is highly expensive and inefficient in terms of
computational cost. However, inspired by biological perception
systems, it is possible to develop an artificial attention model
able to select only the relevant part of the scene, as human
vision does. From the Automated Planning point of view, a
relevant area can be seen as an area where the objects involved
in the execution of a plan are located. Thus, the planning system
should guide the attention model to track relevant objects. But,
at the same time, the perceived objects may constrain or provide
new information that could suggest the modification of a current
plan. Therefore, a plan that is being executed should be adapted
or recomputed taking into account actual information perceived
from the world. In this work, we introduce an architecture that
creates a symbiosis between the planning and the attention
modules of a robotic system, linking visual features with high
level behaviours. The architecture is based on the interaction of
an oversubscription planner, that produces plans constrained
by the information perceived from the vision system, and an
object-based attention system, able to focus on the relevant
objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tec
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to integrate deep learning with explicit language
structures and rules. The key ideas of ATPL are: 1) unsupervised learning of
role-unbinding vectors of words via TPR-based deep neural network; 2) employing
attention modules to compute TPR; and 3) integration of TPR with typical deep
learning architectures including Long Short-Term Memory (LSTM) and Feedforward
Neural Network (FFNN). The novelty of our approach lies in its ability to
extract the grammatical structure of a sentence by using role-unbinding
vectors, which are obtained in an unsupervised manner. This ATPL approach is
applied to 1) image captioning, 2) part of speech (POS) tagging, and 3)
constituency parsing of a sentence. Experimental results demonstrate the
effectiveness of the proposed approach
The Laminar Organization of Visual Cortex: A Unified View of Development, Learning, and Grouping
Why are all sensory and cognitive neocortex organized into layered circuits? How do these layers organize circuits that form functional columns in cortical maps? How do bottom-up, top-down, and horizontal interactions within the cortical layers generate adaptive behaviors. This chapter summarizes an evolving neural model which suggests how these interactions help the visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. It is suggested that the mechanisms which achieve property (3) imply properties of (I) and (2). New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657
- …