64,787 research outputs found
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks
We present a mathematical analysis of the effects of Hebbian learning in
random recurrent neural networks, with a generic Hebbian learning rule
including passive forgetting and different time scales for neuronal activity
and learning dynamics. Previous numerical works have reported that Hebbian
learning drives the system from chaos to a steady state through a sequence of
bifurcations. Here, we interpret these results mathematically and show that
these effects, involving a complex coupling between neuronal dynamics and
synaptic graph structure, can be analyzed using Jacobian matrices, which
introduce both a structural and a dynamical point of view on the neural network
evolution. Furthermore, we show that the sensitivity to a learned pattern is
maximal when the largest Lyapunov exponent is close to 0. We discuss how neural
networks may take advantage of this regime of high functional interest
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks
A long-term goal of AI is to produce agents that can learn a diversity of
skills throughout their lifetimes and continuously improve those skills via
experience. A longstanding obstacle towards that goal is catastrophic
forgetting, which is when learning new information erases previously learned
information. Catastrophic forgetting occurs in artificial neural networks
(ANNs), which have fueled most recent advances in AI. A recent paper proposed
that catastrophic forgetting in ANNs can be reduced by promoting modularity,
which can limit forgetting by isolating task information to specific clusters
of nodes and connections (functional modules). While the prior work did show
that modular ANNs suffered less from catastrophic forgetting, it was not able
to produce ANNs that possessed task-specific functional modules, thereby
leaving the main theory regarding modularity and forgetting untested. We
introduce diffusion-based neuromodulation, which simulates the release of
diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up
or down regulate) learning in a spatial region. On the simple diagnostic
problem from the prior work, diffusion-based neuromodulation 1) induces
task-specific learning in groups of nodes and connections (task-specific
localized learning), which 2) produces functional modules for each subtask, and
3) yields higher performance by eliminating catastrophic forgetting. Overall,
our results suggest that diffusion-based neuromodulation promotes task-specific
localized learning and functional modularity, which can help solve the
challenging, but important problem of catastrophic forgetting
The effects of chaos edge management on intentional organizational forgetting with emphasis on quantum learning (case study: information technology-based organizations)
Unlike learning process, the critical phenomenon of organizational forgetting is not entirely understood. There are two categories of forgetting: accidental forgetting (not-preferred) and intentional forgetting (preferred). Therefore, all the variables that influence intentional forgetting can be important for organizational learning. One of them, which have been neglected thus far, is the edge of chaos in quantum learning. It is the point that a balance is achieved between stability and chaos. Organizational innovation, learning, and creativity all reach a proper level at this point. Along with emphasizing on these variables and surveying an IT-based organization, the present study is an attempt to discover the causal relationships between the variables. Based on the data from 289 filled out questionnaires, of which reliability and validity have been confirmed, structural equations model was developed in AMOS. The results showed that the all path coefficients were significant. In addition, comparison of goodness of fit indices and the standard range showed that all indices were acceptable and the main hypothesis regarding effectiveness of quantum learning on organizational forgetting was supported. The effect of quantum learning on organizational forgetting in non-standard and standard conditions was 0.51 and 0.28, respectively.
Keywords: quantum learning, edge of chaos management, intentional organizational forgetting. JEL Classification: D83, D2
Online Continual Learning on Sequences
Online continual learning (OCL) refers to the ability of a system to learn
over time from a continuous stream of data without having to revisit previously
encountered training samples. Learning continually in a single data pass is
crucial for agents and robots operating in changing environments and required
to acquire, fine-tune, and transfer increasingly complex representations from
non-i.i.d. input distributions. Machine learning models that address OCL must
alleviate \textit{catastrophic forgetting} in which hidden representations are
disrupted or completely overwritten when learning from streams of novel input.
In this chapter, we summarize and discuss recent deep learning models that
address OCL on sequential input through the use (and combination) of synaptic
regularization, structural plasticity, and experience replay. Different
implementations of replay have been proposed that alleviate catastrophic
forgetting in connectionists architectures via the re-occurrence of (latent
representations of) input sequences and that functionally resemble mechanisms
of hippocampal replay in the mammalian brain. Empirical evidence shows that
architectures endowed with experience replay typically outperform architectures
without in (online) incremental learning tasks.Comment: L. Oneto et al. (eds.), Recent Trends in Learning From Data, Studies
in Computational Intelligence 89
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data
Deep Learning has the hierarchical network architecture to represent the
complicated features of input patterns. Such architecture is well known to
represent higher learning capability compared with some conventional models if
the best set of parameters in the optimal network structure is found. We have
been developing the adaptive learning method that can discover the optimal
network structure in Deep Belief Network (DBN). The learning method can
construct the network structure with the optimal number of hidden neurons in
each Restricted Boltzmann Machine and with the optimal number of layers in the
DBN during learning phase. The network structure of the learning method can be
self-organized according to given input patterns of big data set. In this
paper, we embed the adaptive learning method into the recurrent temporal RBM
and the self-generated layer into DBN. In order to verify the effectiveness of
our proposed method, the experimental results are higher classification
capability than the conventional methods in this paper.Comment: 8 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1807.03487, arXiv:1807.0348
Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings
Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating
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