8 research outputs found

    Life Long Learning In Sparse Learning Environments

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    Life long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an algorithm and framework for life long learning among a network of agents in a sparse real-time learning environment. This work will utilize the robust knowledge representation of neural networks, and make use of both functional and representational knowledge transfer to accomplish this task. A new generative life long learning algorithm utilizing cascade correlation and reverberating pseudo-rehearsal and incorporating a method for merging divergent life long learning paths will be implemented

    Transfer Learning using Computational Intelligence: A Survey

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    Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..

    The Roles of Symbols in Neural-based AI: They are Not What You Think!

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    We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly. But, they are also used internally within an agent through a form of self-communication to help formulate, describe and justify subsymbolic patterns of neural activity that truly implement thinking. Symbols, and our languages that make use of them, not only allow us to explain our thinking to others and ourselves, but also provide beneficial constraints (inductive bias) on learning about the world. In this paper we present relevant insights from neuroscience and cognitive science, about how the human brain represents symbols and the concepts they refer to, and how today's artificial neural networks can do the same. We then present a novel neuro-symbolic hypothesis and a plausible architecture for intelligent agents that combines subsymbolic representations for symbols and concepts for learning and reasoning. Our hypothesis and associated architecture imply that symbols will remain critical to the future of intelligent systems NOT because they are the fundamental building blocks of thought, but because they are characterizations of subsymbolic processes that constitute thought.Comment: 28 page

    Achieving continual learning in deep neural networks through pseudo-rehearsal

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    Neural networks are very powerful computational models, capable of outperforming humans on a variety of tasks. However, unlike humans, these networks tend to catastrophically forget previous information when learning new information. This thesis aims to solve this catastrophic forgetting problem, so that a deep neural network model can sequentially learn a number of complex reinforcement learning tasks. The primary model proposed by this thesis, termed RePR, prevents catastrophic forgetting by introducing a generative model and a dual memory system. The generative model learns to produce data representative of previously seen tasks. This generated data is rehearsed, while learning a new task, through a process called pseudo-rehearsal. This process allows the network to learn the new task, without forgetting previous tasks. The dual memory system is used to split learning into two systems. The short-term system is only responsible for learning the new task through reinforcement learning and the long-term system is responsible for retaining knowledge of previous tasks, while being taught the new task by the short-term system. The RePR model was shown to learn and retain a short sequence of reinforcement tasks to above human performance levels. Additionally, RePR was found to substantially outcompete state-of-the-art solutions and prevent forgetting similarly to a model which rehearsed real data from previously learnt tasks. RePR achieved this without: increasing in memory size as the number of tasks expands; revisiting previously learnt tasks; or directly storing data from previous tasks. Further results showed that RePR could be improved by informing the generator which image features are most important to retention and that, when challenged by a longer sequence of tasks, RePR would typically demonstrate gradual forgetting rather than dramatic forgetting. Finally, results also demonstrated RePR can successfully be adapted to other deep reinforcement learning algorithms

    Transfer rule learning for biomarker discovery and verification from related data sets

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    Biomarkers are a critical tool for the detection, diagnosis,monitoring and prognosis of diseases, and for understandingdisease mechanisms in order to create treatments. Unfortunately,finding reliable biomarkers is often hampered by a number of practicalproblems, including scarcity of samples, the high dimensionality of the data, and measurement error. An important opportunity to make the most ofthese scarce data is to combine information from multiple relateddata sets for more effective biomarker discovery. Because the costsof creating large data sets for every disease of interest are likelyto remain prohibitive, methods for more effectively making use ofrelated biomarker data sets continues to be important.This thesis develops TRL, a novel framework for integrative biomarkerdiscovery from related but separate data sets, such as those generatedfor similar biomarker profiling studies. TRL alleviates the problemof data scarcity by providing a way to validateknowledge learned from one data set and simultaneously learn newknowledge on a related data set. Unlike other transfer learningapproaches, TRL takes prior knowledge in the form of interpretable,modular classification rules, and uses them to seed learning on a newdata set.We evaluated TRL on 13 pairs of real-world biomarker discovery datasets, and found TRL improves accuracy twice as often asdegrading it. TRL consists of four alternative methods for transferand three measures of the amount of information transferred. Byexperimenting with these methods, we investigate the kinds ofinformation necessary to preserve for transfer learning from relateddata sets. We found it is important to keep track of therelationships between biomarker values and disease state, and toconsider during learning how rules will interact in the final model.If the source and target data are drawn from the same distribution, wefound the performance improvement and amount of transfer increase withincreasing size of the source compared to the target data

    Modelling evolvability in genetic programming

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    We develop a tree-based genetic programming system, capable of modelling evolvability during evolution through artificial neural networks (ANN) and exploiting those networks to increase the generational fitness of the system. This thesis is empirically focused; we study the effects of evolvability selection under varying conditions to demonstrate the effectiveness of evolvability selection. Evolvability is the capacity of an individual to improve its future fitness. In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability. We construct a system, Sample-Evolvability Genetic Programming (SEGP), that estimates the true evolvability of a program by conducting a limited number of evolvability samples. Evolvability is sampled by conducting a number of genetic operations upon a program and comparing the fitnesses of resulting programs with the original. SEGP is able to achieve an increase in fitness at a cost of increased computational complexity. We then construct a system which improves upon SEGP, Model-Evolvability Genetic Programming (MEGP), that models the true evolvability of a program by training an ANN to predict its evolvability. MEGP reduces the computational cost of sampling evolvability while maintaining the fitness gains. MEGP is empirically shown to improve generational fitness for a streaming domain, in exchange for an upfront increase in computational time

    Recueil des CICC-Hebdo / Année 2017

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    Parmi nos instruments de communication, le CICC-Hebdo est un outil synthétique, incontournable en termes de partage d’informations. Plusieurs témoignages attestent du dynamisme qu’il génère dans le champ de la criminologie. Nos abonnés viennent de tous les continents et se comptent parmi des chercheurs universitaires, des étudiants, des diplômés, des partenaires, des organismes à but non lucratif et gouvernementaux. Nos articles sont publiés en français, en anglais ou espagnol, dépendamment de la langue dans laquelle se déroule l’activité ou selon le pays. Dans ce recueil, vous retrouverez tous les numéros du volume 10, soit une année d’informations criminologiques.Fonds de recherche sur la société et la culture du Québec (FQRSC); Université de MontréalCICC-Hebdo, vol. 10, no 1 à no 51-5
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