Discovery Radiomics with Recurrent Next Blocks


Discovery Radiomics with Recurrent Next Blocks – The recent success of neural networks (NN) learning is a strong example of the need for developing a suitable model for data mining and the need to design models capable of robustly detecting and exploiting unseen features of the environment. In this paper, we propose a novel neural network model, dubbed MNN, which learns and learns to predict what information in a given network is being inferred or mined. MNN is very flexible for modeling large networks, and it can easily be adapted to particular situations. We provide a simple neural network architecture, based on recurrent neural networks, for MNN, with an energy minimizer that can be dynamically tuned based on the network model. We demonstrate the effectiveness of the proposed method on classification of a set of synthetic images taken by a wearable smartwatch equipped with an external sensor.

This paper describes a new methodology for automatic lexical variation based on the assumption of a non-monotonic form of lexical semantics. The methodology has two components: a new lexical semantics for the context (syntax) based semantics, which models the syntactic semantics of language using an unifying set of lexical semantics, and a set of lexical semantics for the language-dependent semantics (meaning) based on the context-dependent semantics. The algorithm is applied to a problem of word-level lexical variation in a standard corpus and a novel system for studying language-independent variation of discourse, called the Topic-independent Semantic Semantics (TSS) database.

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Discovery Radiomics with Recurrent Next Blocks

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  • A Nonparametric Coarse-Graining Approach to Image Denoising

    The Evolution of Lexical Variation: Does Language Matter?This paper describes a new methodology for automatic lexical variation based on the assumption of a non-monotonic form of lexical semantics. The methodology has two components: a new lexical semantics for the context (syntax) based semantics, which models the syntactic semantics of language using an unifying set of lexical semantics, and a set of lexical semantics for the language-dependent semantics (meaning) based on the context-dependent semantics. The algorithm is applied to a problem of word-level lexical variation in a standard corpus and a novel system for studying language-independent variation of discourse, called the Topic-independent Semantic Semantics (TSS) database.


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