This proposed ANN-based model combines a convolutional neural network (CNN) and generative adversarial network (GAN). Through learning of the similarity between input paired data, the CNN part only requires few raw data to achieve a good performance, suitable for a classification task. The GAN part is used to extract important information from.
In particular we suggest a classification of different types of models used in engineering design and compare them to models used in scientific research. Thereby we do not aim at an encompassing map of models in engineering practice, but we aim to identify key categories of models with regards to their relationship to their targets. We contend that the functions of models in engineering design.
A Survey of Audio-based Music Classification and Annotation.. An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model. (2004). Artist classification with webbased data. In: (2007). Audio Information Retrieval using Semantic Similarity. (2005). Automated image annotation using global features and robust non-parametric density estimation.
Several research efforts have been done in pattern classification. Most of the works based on generative model. There are Dynamic Time Warping (DTW) (3), Hidden Markov Models (HMM), Vector Quantization (VQ) (4), Gaussian mixture model (GMM) (5) and so forth. Generative model is for randomly generating observed data, with some hidden parameters. Because of the randomly generating observed data.
Effective Estimation of Deep Generative Language Models. Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction; Clinical Concept Linking with Contextualized Neural Representations; Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain; Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling; Code-switching patterns.Learn More
Probabilistic classifiers use mixture models for classification. The mixture model assumes that each class is a component of the mixture. Each mixture component is a generative model that provides the probability of sampling a particular term for that component. These kinds of classifiers are also called generative classifiers. Three of the most famous probabilistic classifiers are discussed.Learn More
However, if one looks at some models as they are promoted in empirical aesthetics (e.g., Leder et al., 2004, 2015; Leder and Nadal, 2014; Redies, 2015), they do not match the necessary criterium of similarity. Suppes (1960, p. 13) has been rather outspoken and may be overdoing the case when he writes: “The attempt to characterize exactly.Learn More
In 1680 AD, the idea of classification by appearance slowly began to permeate society as lawmakers in the early colonies of North America began to use “white” as a classification of themselves rather than “Englishmen” or “Christians.” 1776 AD marks a turning point in the history of race in which the word “Caucasian” was first used by a man named Johann Blumenbach in his work On.Learn More
Classification of the Generative Actions of a Story and Mental Space. Based on the above concept, we can classify generative actions in a mental world. According to the structural properties of a story and the notion of a mental space, we can classify generative actions into five basic types: connective, hierarchical, contextual, gathering, and adaptive. The last type (adaptive) drives the.Learn More
Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014.Learn More
Rule-based systems are also difficult to maintain and don’t scale well given that adding new rules can affect the results of the pre-existing rules. Machine Learning Based Systems. Instead of relying on manually crafted rules, text classification with machine learning learns to make classifications based on past observations. By using pre-labeled examples as training data, a machine learning.Learn More
This essay will discuss agent-based modeling (ABM) and its potential as a technique for studying history, including literary history. When confronted by historical simulations, scholars first notice their unusual ontological commitments: a computer model of social life creates a simulated world and then subjects that world to analysis. On the surface, computational modeling has many of the.Learn More
SEMI-SUPERVISED LEARNING BASED ON HIERARCHICAL GENERATIVE MODELS FOR END-TO-END SPEECH SYNTHESIS: 4119: SEMI-SUPERVISED LEARNING FOR TEXT CLASSIFICATION BY LAYER PARTITIONING: 4471: Semi-supervised learning of processes over multi-relational graphs: 4194: Semi-supervised optimal transport methods for detecting anomalies: 5113.Learn More
Tables of content are generated automatically and are based on records of articles contained that are available in the TIB-Portal index. Due to missing records of articles, the volume display may be incomplete, even though the whole journal is available at TIB.Learn More
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries Zhewei Yao, Amir Gholami,. Deep Generative Models for Distribution-Preserving Lossy Compression Michael Tschannen, Eirikur Agustsson, Mario Lucic; Exact natural gradient in deep linear networks and its application to the nonlinear case Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin; Constructing Fast Network.Learn More
Previous work on similarity has focused on color, form, or specific objects and neglected a content-based analysis. Presently, the collaboration between computer vision and art history considers similarity based on content—the Computer Vision group being no exception. Art historians, on the other hand, also regard brushstroke, material, technique, and content to determine if images are.Learn More