Views: 4687Paper: 37
International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials.
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SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.
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Self-supervised learning is essentially a method of unsupervised learning, and we will set up a "Pretext" and construct Pesdeo Labels to train the network model according to some characteristics of data. The self-supervised model can be used as a pre-training model for other learning tasks to provide a better initial training area. Therefore, self-supervised learning can also be regarded as a general visual representation for learning images.
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Medical big data is a combination of big data, machine learning, deep learning and other technologies and disciplines such as evidence-based medicine and imaging omics. It can provide solutions for scenarios such as health management and auxiliary diagnosis and treatment.
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Financial big data takes financial data as the core and financial data as the core. It targets banking, securities, insurance, payment and clearing, Internet finance and other industries to improve resource allocation efficiency, strengthen risk control capabilities, and promote business innovation. A new generation of information technology and service formats for acquisition, storage, analysis and application.
Views: 339Paper: 127
When some object X is said to be embedded in another object Y, the embedding is given by some injective and structure-preserving map f : X → Y. The precise meaning of 'structure-preserving' depends on the kind of mathematical structure of which X and Y are instances. In the terminology of category theory, a structure-preserving map is called a morphism.
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'Computer Vision' is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, ''e.g.'', in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, Computer vision, learning, indexing, motion estimation, and image restoration.
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Reinforcement Learning is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take ''actions'' in an ''environment'' so as to maximize some notion of cumulative ''reward''. The problem is studied in many other disciplines, such as game theory, control theory, operations research, information theory, and simulation-based optimization. In the operations research and control literature, reinforcement learning is called ''approximate dynamic programming,'' The approach has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with learning or approximation. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. In machine learning, the environment is typically formulated as a Reinforcement learning (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on performance,, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and infinite MDPs.
Views: 860Paper: 129
'Deep Learning' (also known as 'deep structured learning' or 'hierarchical learning') is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning models are loosely related to information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Deep learning architectures such as deep neural networks, deep belief networks, and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, and drug design, where they have produced results comparable to and in some cases superior to human experts.
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Information Retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Information Recommendation seeks to predict the "rating" or "preference" a user would give to an item. It is primarily used in commercial applications.
Views: 1330Paper: 134
'Natural-language processing' ('NLP') is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.
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CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
Views: 1185Paper: 15
The purpose of the Neural Information Processing Systems annual meeting is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field.
Views: 536Paper: 19
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.