Parallel processing of run-length-encoded Boolean imagery: linear transformations and high-level image operations

1993 ◽  
Author(s):  
Mark S. Schmalz
Keyword(s):  
Parallel Processing ◽  
Linear Transformations ◽  
Run Length ◽  
High Level

Parallel processing in high-level categorization of natural images

10.1038/nn866 ◽  
2002 ◽  
Vol 5 (7) ◽  
pp. 629-630 ◽  
Author(s):  
Guillaume A. Rousselet ◽  
Michèle Fabre-Thorpe ◽  
Simon J. Thorpe
Keyword(s):  
Parallel Processing ◽  
Natural Images ◽  
High Level

scholarly journals Kinome-wide activity classification of small molecules by deep learning

2019 ◽  
Author(s):  
Bryce K Allen ◽  
Nagi G Ayad ◽  
Stephan C Schürer
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Small Molecules ◽  
Small Molecule ◽  
Kinase Inhibitors ◽  
Structural Features ◽  
Linear Transformations ◽  
Resistant Disease ◽  
Small Molecule Inhibition ◽  
High Level

Deep learning is a machine learning technique that attempts to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. However, the application of deep learning to discriminating features of kinase inhibitors has not been well explored. Small molecule kinase inhibitors are an important class of anti-cancer agents and have demonstrated impressive clinical efficacy in several different diseases. However, resistance is often observed mediated by adaptive Kinome reprogramming or subpopulation diversity. Therefore, polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant disease. Their development would benefit from more comprehensive and dense knowledge of small-molecule inhibition across the human Kinome. Because such data is not publicly available, we evaluated multiple machine learning methods to predict small molecule inhibition of 342 kinases using over 650K aggregated bioactivity annotations for over 300K small molecules curated from ChEMBL and the Kinase Knowledge Base (KKB). Our results demonstrated that multi-task deep neural networks outperform classical single-task methods, offering potential towards predicting activity profiles and filling gaps in the available data.

scholarly journals The Realization of Rapid Median Filter Algorithm on FPGA

2012 ◽  
Vol 6-7 ◽  
pp. 659-664
Author(s):  
En Shun Kang ◽  
Yu Xi Zhao
Keyword(s):  
Image Processing ◽  
Parallel Processing ◽  
Real Time ◽  
Processing Time ◽  
Median Filter ◽  
Processing System ◽  
Processing Capability ◽  
Real Time Image ◽  
High Level ◽  
Time Image

Traditional median filter algorithm has the long processing time, which goes against the real-time image processing. According to its shortcomings, this paper puts forward the rapid median filter algorithm, and uses DE2 board of the company called Altera to do the realization on FPGA (CycloneII 2C35). The experimental results show that the image pre-processing system is able to complete a variety of high-level image algorithms in milliseconds, and FPGA's parallel processing capability and pipeline operations can dramatically improve the speed of image processing, so the FPGA-based image processing system has broad prospects for development．

A Brief Review on Deep Learning and Types of Implementation for Deep Learning

2018 ◽  
pp. 20-32 ◽  
Author(s):  
Uthra Kunathur Thikshaja ◽  
Anand Paul
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Linear Transformations ◽  
Deep Architecture ◽  
Implementation Techniques ◽  
Artificial Neural Network Ann ◽  
High Level ◽  
Multiple Processing

In recent years, there's been a resurgence in the field of Artificial Intelligence and deep learning is gaining a lot of attention. Deep learning is a branch of machine learning based on a set of algorithms that can be used to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations. Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. This chapter describes the motivations for deep architecture, problem with large networks, the need for deep architecture and new implementation techniques for deep learning. At the end, there is also an algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.

A Brief Review on Deep Learning and Types of Implementation for Deep Learning

2020 ◽  
pp. 30-40
Author(s):  
Uthra Kunathur Thikshaja ◽  
Anand Paul
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Linear Transformations ◽  
Deep Architecture ◽  
Implementation Techniques ◽  
Artificial Neural Network Ann ◽  
High Level ◽  
Multiple Processing

In recent years, there's been a resurgence in the field of Artificial Intelligence and deep learning is gaining a lot of attention. Deep learning is a branch of machine learning based on a set of algorithms that can be used to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations. Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. This chapter describes the motivations for deep architecture, problem with large networks, the need for deep architecture and new implementation techniques for deep learning. At the end, there is also an algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.

scholarly journals Platform Independence 2010 - Helping Documents Fly Well in Emerging Architectures

2010 ◽  
Author(s):  
Ann Wrightson
Keyword(s):  
Parallel Processing ◽  
Data Structures ◽  
Parallel Architectures ◽  
Information Design ◽  
Design Heuristics ◽  
Platform Architecture ◽  
Level Information ◽  
Platform Independence ◽  
High Level

Parallel processing and memory bottlenecks dominate current platform architecture conversations. After many years on the sidelines, parallel architectures are rapidly becoming mainstream, with more parallelism the obvious way to gain yet more performance. Feeding data to and from all these parallel cycles is also becoming more challenging. What does this have to do with XML? Surely all this is under the hood, something for compiler designers, software architects and other non-content people to worry about? The answer is that these issues can't be totally hidden under the hood. Balisageurs as content-folks and interoperability-folks need to pay attention now to the high level information design heuristics that will prevent our data structures being the ones that happen to run like treacle (or molasses) on the coming generations of faster, larger and neater systems.

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