126,628 research outputs found
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Deep learning-based i-EEG classification with convolutional neural networks for drug-target interaction prediction
Drug-target interaction (DTI) prediction has become a foundational task in
drug repositioning, polypharmacology, drug discovery, as well as drug
resistance and side-effect prediction. DTI identification using machine
learning is gaining popularity in these research areas. Through the years,
numerous deep learning methods have been proposed for DTI prediction.
Nevertheless, prediction accuracy and efficiency remain key challenges.
Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the
development of central nervous system-active drugs. Quantitative EEG analysis
demonstrates high reliability in studying the effects of drugs on the brain.
Earlier preclinical pharmaco-EEG studies showed that different types of drugs
can be classified according to their mechanism of action on neural activity.
Here, we propose a convolutional neural network for EEG-mediated DTI
prediction. This new approach can explain the mechanisms underlying complicated
drug actions, as it allows the identification of similarities in the mechanisms
of action and effects of psychotropic drugs
ROBUSTNESS AND PREDICTION ACCURACY OF MACHINE LEARNING FOR OBJECTIVE VISUAL QUALITY ASSESSMENT
Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptimal. A Principal Component Regression based algorithm and a Feed Forward Neural Network are compared when pooling the Structural Similarity Index (SSIM) features perturbed with noise. The neural network adapts better with noise and intrinsically favours features according to their salient content
Continual Lifelong Learning with Neural Networks: A Review
Humans and animals have the ability to continually acquire, fine-tune, and
transfer knowledge and skills throughout their lifespan. This ability, referred
to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
that together contribute to the development and specialization of our
sensorimotor skills as well as to long-term memory consolidation and retrieval.
Consequently, lifelong learning capabilities are crucial for autonomous agents
interacting in the real world and processing continuous streams of information.
However, lifelong learning remains a long-standing challenge for machine
learning and neural network models since the continual acquisition of
incrementally available information from non-stationary data distributions
generally leads to catastrophic forgetting or interference. This limitation
represents a major drawback for state-of-the-art deep neural network models
that typically learn representations from stationary batches of training data,
thus without accounting for situations in which information becomes
incrementally available over time. In this review, we critically summarize the
main challenges linked to lifelong learning for artificial learning systems and
compare existing neural network approaches that alleviate, to different
extents, catastrophic forgetting. We discuss well-established and emerging
research motivated by lifelong learning factors in biological systems such as
structural plasticity, memory replay, curriculum and transfer learning,
intrinsic motivation, and multisensory integration
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
The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth
PredictingTreeGrowth is free and open-source application software written in Python 3.7 that allows easy and fast development of predictive models using the Recurrent Neural Network (RNN)/Long Short-Term Memory (LSTM) framework. RNNs have an upgraded architecture able to capture tree growth mechanisms related to time ordering and size dependence. The motivation for this App is to demystify the use of Machine Learning algorithms and allow accessibility of Machine Learning algorithms by the scientific community. Its simple graphical user interface (GUI) provides straightforward tools for building predictive models with the RNN algorithm.Fil: Magalhaes, Juliana G. de S.. University of British Columbia; CanadáFil: Polinko, Adam P.. Mississippi State University.; Estados UnidosFil: Amoroso, Mariano Martin. Universidad Nacional de Río Negro. Sede Andina. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kohli, Gursimran S.. University Fraser Simon; CanadáFil: Larson, Bruce C.. University of British Columbia; Canad
ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, 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, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, 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, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
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