Artificial neural networks | Deep learning
Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain. At the core of HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM constantly learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). When applied to computers, HTM is well suited for prediction, anomaly detection, classification, and ultimately sensorimotor applications. HTM has been tested and implemented in software through example applications from Numenta and a few commercial applications from Numenta's partners. (Wikipedia).
More Info: https://www.caltech.edu/about/news/where-are-my-keys-and-other-memory-based-choices-probed-brain The brain’s memory-retrieval network is composed of many interacting regions. In a new study, Caltech researchers looked at the interaction between two nodes in this network: the me
From playlist Our Research
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
Post-analysis temporal downsampling
Are your time-frequency results matrices too big? Watch this video to learn how to reduce the temporal resolution of your results to match their temporal precision, which can save lots of time and space. The video uses files you can download from https://github.com/mikexcohen/ANTS_youtube
From playlist OLD ANTS #5) Normalization and time-frequency post-processing
Queue Data Structure – Algorithms
This is an explanation of the dynamic data structure known as a queue. It compares a linear queue implemented by means of a dynamic array with a linear queue implemented with a static array. It also includes an explanation of how a circular queue works, along with pseudocode for the enqu
From playlist Data Structures
From playlist CS294-112 Deep Reinforcement Learning Sp17
Recorded: Spring 2014 Lecturer: Dr. Erin M. Buchanan Materials: created for Memory and Cognition (PSY 422) using Smith and Kosslyn (2006) Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/other-courses/
From playlist PSY 422 Memory and Cognition with Dr. B
Mixed-effect model for the spatiotemporal analysis of longitudinal (...) - Workshop 2 - CEB T1 2019
Stéphanie Allassonnière (Univ. Paris Descartes) / 13.03.2019 Mixed-effect model for the spatiotemporal analysis of longitudinal manifold-valued data. In this talk, I propose to present a generic hierarchical spatiotemporal model for longitudinal manifold-valued data, which consists in r
From playlist 2019 - T1 - The Mathematics of Imaging
Neuroscience in the Wolfram Language
To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technology-conference/ Speaker: Keiko Hirayama Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, an
From playlist Wolfram Technology Conference 2017
Linux Memory Management at Scale
Memory management is an extraordinarily complex and widely misunderstood topic. It is also one of the most fundamental concepts to understand in order to produce coherent, stable, and efficient systems and containers, especially at scale. In this talk, we will go over how to compose reliab
From playlist Infrastructure
Sudipto Banerjee: High-dimensional Bayesian geostatistics
Abstract: With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarc
From playlist Probability and Statistics
Hierarchical reinforcement learning - Doina Precup
Doina Precup research interests are in the areas of reinforcement learning, deep learning, time series analysis, and diverse applications. In this talk, Dr. Precup reviews how hierarchical reinforcement learning refers to a class of computational methods that enable artificial agents that
From playlist Wu Tsai Neurosciences Institute
05 Spatial Data Analytics: Declustering
Walkthrough of a spatial data declustering (geostatistics) workflow in Python / Jupyter Notebook Slides with the GeostatsPy Python Package to correct for spatial sampling bias.
From playlist Spatial Data Analytics and Modeling
Robert Palovics - Temporal walk based centrality metrics for graph streams
https://indico.math.cnrs.fr/event/3475/attachments/2180/2567/Palovics_GomaxSlides.pdf
From playlist Google matrix: fundamentals, applications and beyond
Video Classification with Deep Learning
This video will explain how to use Deep Convolutional Neural Networks to classify Videos. Thanks for watching, please subscribe for more videos on Deep Learning! Paper Link: Large-scale Video Classification with Convolutional Neural Networks: https://static.googleusercontent.com/media/r
From playlist Deep Learning Paper Summaries
Hierarchical Clustering - Unsupervised Learning and Clustering
This video is about Hierarchical Clustering - Unsupervised Learning and Clustering
From playlist Machine Learning
CERIAS Security: A Generalized Temporal Role Based Access Control Model 4/5
Clip 4/5 Speaker: James Joshi · Pittsburgh University A key issue in computer system security is to protect information against unauthorized access. Emerging workflow-based applications in healthcare, manufacturing, the financial sector, and e-commerce inherently have complex, time-ba
From playlist The CERIAS Security Seminars 2004