Views: 103Paper: 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.
Views: 662Paper: 111
'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.
Views: 370Paper: 101
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: 551Paper: 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.
Views: 1163Paper: 165
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: 917Paper: 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.
Views: 2776Paper: 36
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.
Views: 1057Paper: 23
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: 908Paper: 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: 401Paper: 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.
Views: 203Paper: 46
The Association for the Advancement of Artificial Intelligence. Formerly known as the American Association for Artificial Intelligence, it is a non-profit academic research organization dedicated to promoting scientific research into the nature of intelligent behavior.
Views: 155Paper: 9
The ACM Conference on Computer and Communications Security (CCS) is the flagship annual conference of the Special Interest Group on Security, Audit and Control (SIGSAC) of the Association for Computing Machinery (ACM). The conference brings together information security researchers, practitioners, developers, and users from all over the world to explore cutting-edge ideas and results. It provides an environment to conduct intellectual discussions. From its inception, CCS has established itself as a high standard research conference in its area.
Views: 359Paper: 246
The ACM CHI Conference on Human Factors in Computing Systems is the premier international conference of Human-Computer Interaction. CHI – pronounced ‘kai’ – is a place where researchers and practitioners gather from across the world to discuss the latest in interactive technology. We are a multicultural community from highly diverse backgrounds who together investigate new and creative ways for people to interact. CHI 2019 was the first time that CHI has taken place in the UK and was hosted at the Scottish Event Campus in Glasgow.
Views: 145Paper: 14
Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.