Under review by a JournalHTM-MAT is a MATLAB®-based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper presents an implementation of HTM-MAT with several illustrative examples including several toy datasets and compared with two sequence learning applications employing state-of-the-art algorithms-the recurrentjs based on the Long Short-Term Memory (LSTM) algorithm and OS-ELM which is based on an online sequential version of the Extreme Learning Machine. The performance of HTM-MAT using two historical ...
Sequence learning, prediction and replay have been proposed to constitute the universal computations...
In a quest for modeling human brain, we are going to introduce a brain model based on a general fram...
This paper demonstrates the development of an Long-Short Term Memory (LSTM) network and its applicat...
Machine learning is widely used on stored data, recently it is developed to model real time streams....
AbstractThe recent development in the theory of Hierarchical Temporal Memory (HTM) - Cortical Learni...
These slides were presented at the 1st High Performance Machine Learning (HPML) workshop, held in c...
Abstract — Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition co...
Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and ...
Over the years, and with the emergence of various technological innovations, the relevance of automa...
Over the years, and with the emergence of various technological innovations, the relevance of automa...
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to ...
A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predic...
In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is ap-plied to the problem of l...
Sequence learning, prediction and replay have been proposed to constitute the universal computations...
Sequence learning, prediction and generation has been proposed to be the universal computation perfo...
Sequence learning, prediction and replay have been proposed to constitute the universal computations...
In a quest for modeling human brain, we are going to introduce a brain model based on a general fram...
This paper demonstrates the development of an Long-Short Term Memory (LSTM) network and its applicat...
Machine learning is widely used on stored data, recently it is developed to model real time streams....
AbstractThe recent development in the theory of Hierarchical Temporal Memory (HTM) - Cortical Learni...
These slides were presented at the 1st High Performance Machine Learning (HPML) workshop, held in c...
Abstract — Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition co...
Recently the improved bag of features (BoF) model with locality-constrained linear coding (LLC) and ...
Over the years, and with the emergence of various technological innovations, the relevance of automa...
Over the years, and with the emergence of various technological innovations, the relevance of automa...
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to ...
A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predic...
In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is ap-plied to the problem of l...
Sequence learning, prediction and replay have been proposed to constitute the universal computations...
Sequence learning, prediction and generation has been proposed to be the universal computation perfo...
Sequence learning, prediction and replay have been proposed to constitute the universal computations...
In a quest for modeling human brain, we are going to introduce a brain model based on a general fram...
This paper demonstrates the development of an Long-Short Term Memory (LSTM) network and its applicat...