AIMC Topic: Memory

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A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn's Self-Nomination.

Computational intelligence and neuroscience Mar 17, 2021
As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training ...
Neural Networks, Computer Algorithms Memory
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Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique.

Computational and mathematical methods in medicine Mar 16, 2021
Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic F...
Natural Language Processing Humans Semantics Pattern Recognition, Automated Neural Networks, Computer Computational Biology Algorithms Models, Neurological Learning Machine Learning Memory Electronic Data Processing Neocortex
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Quantization Friendly MobileNet (QF-MobileNet) Architecture for Vision Based Applications on Embedded Platforms.

Neural networks : the official journal of the International Neural Network Society Dec 29, 2020
Deep Neural Networks (DNNs) have become popular for various applications in the domain of image and computer vision due to their well-established performance attributes. DNN algorithms involve powerful multilevel feature extractions resulting in an e...
Algorithms Deep Learning Pattern Recognition, Automated Humans Neural Networks, Computer Memory Photic Stimulation
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A comprehensive study of class incremental learning algorithms for visual tasks.

Neural networks : the official journal of the International Neural Network Society Dec 8, 2020
The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i.e., the tendency of neural networks t...
Humans Neural Networks, Computer Artificial Intelligence Psychomotor Performance Algorithms Memory Visual Perception Reproducibility of Results
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A Brain-Inspired Framework for Evolutionary Artificial General Intelligence.

IEEE transactions on neural networks and learning systems Nov 30, 2020
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this...
Cognition Neuronal Plasticity Memory Neural Networks, Computer Biological Evolution Learning Artificial Intelligence Neurons Algorithms Humans Animals Brain Genomics
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Image memorability is predicted by discriminability and similarity in different stages of a convolutional neural network.

Learning & memory (Cold Spring Harbor, N.Y.) Nov 16, 2020
The features of an image can be represented at multiple levels-from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiment...
Psychomotor Performance Memory Photic Stimulation Visual Perception Judgment Humans Female Young Adult Male Neural Networks, Computer Mental Recall Prospective Studies Discrimination, Psychological
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Echo Memory-Augmented Network for time series classification.

Neural networks : the official journal of the International Neural Network Society Nov 7, 2020
Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo s...
Neural Networks, Computer Nonlinear Dynamics Memory Deep Learning Time Factors
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Embracing Change: Continual Learning in Deep Neural Networks.

Trends in cognitive sciences Nov 3, 2020
Artificial intelligence research has seen enormous progress over the past few decades, but it predominantly relies on fixed datasets and stationary environments. Continual learning is an increasingly relevant area of study that asks how artificial sy...
Learning Memory Humans Neural Networks, Computer Artificial Intelligence
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Capturing human categorization of natural images by combining deep networks and cognitive models.

Nature communications Oct 27, 2020
Human categorization is one of the most important and successful targets of cognitive modeling, with decades of model development and assessment using simple, low-dimensional artificial stimuli. However, it remains unclear how these findings relate t...
Models, Psychological Cognition Humans Judgment Visual Cortex Pattern Recognition, Visual Deep Learning Decision Making Memory
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Overparameterized neural networks implement associative memory.

Proceedings of the National Academy of Sciences of the United States of America Oct 16, 2020
Identifying computational mechanisms for memorization and retrieval of data is a long-standing problem at the intersection of machine learning and neuroscience. Our main finding is that standard overparameterized deep neural networks trained using st...
Nonlinear Dynamics Memory Machine Learning Computational Biology Neural Networks, Computer
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