«machine learning ml»
۷۵۷۳ نتیجهToward a General Solution to the Symbol Grounding Problem: Combining Machine Learning and Computer Vision
The symbol grounding problem, described recently by Harnad, states that the symbols which a traditional AI system manipulates are meaningless to the system, the system thus being dependent on a human operator to interpret the results of its computations. The solution Harnad sugge
Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain
Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use
Strategies and Principles of Distributed Machine Learning on Big Data
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent featur
An Interactive Machine Learning Framework
Machine learning (ML) is believed to be an effective and efficient tool to build reliable prediction model or extract useful structure from an avalanche of data. However, ML is also criticized by its difficulty in interpretation and complicated parameter tuning. In contrast, visu
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Governments and businesses increasingly rely on data analytics and machine learning (ML) for improving their competitive edge in areas such as consumer satisfaction, threat intelligence, decision making, and product efficiency. However, by cleverly corrupting a subset of data use
Machine Learning as Statistical Data Assimilation
We identify a strong equivalence between neural network based machine learning (ML) methods and the formulation of statistical data assimilation (DA), known to be a problem in statistical physics. DA, as used widely in physical and biological sciences, systematically transfers in
A high-bias, low-variance introduction to Machine Learning for physicists
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The revie
Knowledge Extraction from Text: Machine Learning for Text-to-rule Translation
Learning from texts is a noble but distant research goal. We investigate the practicality of a more modest enterprise in which machine learning (ML) and natural language processing (NLP) would mutually reinforce each other. We are designing a system that will process technical ex
MLbase: A Distributed Machine-learning System
Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming—many users do not understand the trade-offs and challenges of parame
Emerging Machine Learning Techniques in Signal Processing
In the era of knowledge-based society and machine automation , there is a strong interest in machine learning (ML) techniques in a wide range of applications. The attention paid to ML methods within the DSP community is not new. Speech recognition is an example of an area where D