+

Kowadlo et al., 2019 - Google Patents

AHA! an'Artificial Hippocampal Algorithm'for Episodic Machine Learning

Kowadlo et al., 2019

View PDF
Document ID
10326965252540549912
Author
Kowadlo G
Ahmed A
Rawlinson D
Publication year
Publication venue
arXiv preprint arXiv:1909.10340

External Links

Snippet

The majority of ML research concerns slow, statistical learning of iid samples from large, labelled datasets. Animals do not learn this way. An enviable characteristic of animal learning isepisodic'learning-the ability to memorise a specific experience as a composition …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/049Temporal neural nets, e.g. delay elements, oscillating neurons, pulsed inputs
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Du et al. Reconstructing perceived images from human brain activities with Bayesian deep multiview learning
Benuwa et al. A review of deep machine learning
Zhang et al. Adding attentiveness to the neurons in recurrent neural networks
Yan et al. Driving posture recognition by convolutional neural networks
O'Connor et al. Real-time classification and sensor fusion with a spiking deep belief network
He et al. Constructing an associative memory system using spiking neural network
Tadeusiewicz et al. Exploring neural networks with C
Deco et al. Attention, short-term memory, and action selection: a unifying theory
Krueger et al. Flexible shaping: How learning in small steps helps
Kim et al. Deep sparse coding for invariant multimodal halle berry neurons
Jiang et al. Shallow unorganized neural networks using smart neuron model for visual perception
Panda et al. Convolutional spike timing dependent plasticity based feature learning in spiking neural networks
Kowadlo et al. AHA! an'Artificial Hippocampal Algorithm'for Episodic Machine Learning
De Cesarei et al. Do humans and deep convolutional neural networks use visual information similarly for the categorization of natural scenes?
Roudi et al. Learning with hidden variables
Whitney Disentangled representations in neural models
Burns Semantically-correlated memories in a dense associative model
Sledge et al. Faster convergence in deep-predictive-coding networks to learn deeper representations
Frady et al. Resonator networks for factoring distributed representations of data structures
Thiele et al. A timescale invariant STDP-based spiking deep network for unsupervised online feature extraction from event-based sensor data
Melchior et al. A neural network model for online one-shot storage of pattern sequences
Kowadlo et al. Unsupervised one-shot learning of both specific instances and generalised classes with a hippocampal architecture
Pham Understanding Human Imagination Through Diffusion Model
Kowadlo et al. One-shot learning for the long term: consolidation with an artificial hippocampal algorithm
Turcsany et al. Modelling Retinal Feature Detection With Deep Belief Networks In A Simulated Environment.
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载