Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Anupriya Gogna"'
Autor:
Anupriya Gogna, Angshul Majumdar
Publikováno v:
IEEE Access, Vol 3, Pp 2803-2813 (2015)
Sparsity of the ratings available in the recommender system database makes the task of rating prediction a highly underdetermined problem. This poses a limit on the accuracy and the quality of prediction. In this paper, we utilize secondary informati
Externí odkaz:
https://doaj.org/article/c834ce44809246cdb33bbf6595bc5a4c
Publikováno v:
Sensors, Vol 14, Iss 9, Pp 15729-15748 (2014)
We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous stu
Externí odkaz:
https://doaj.org/article/6a302e9b46144cdbb2d72a471024c303
In this work we propose an autoencoder based framework for simultaneous reconstruction and classification of biomedical signals. Previously these two tasks, reconstruction and classification were treated as separate problems. This is the first work t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f58f7745111bb9f797a56305b3659e96
http://arxiv.org/abs/1912.12127
http://arxiv.org/abs/1912.12127
Autor:
Anupriya Gogna, Angshul Majumdar
Publikováno v:
Knowledge-Based Systems. 125:83-95
Unified framework for joint accuracy-diversity optimization in recommender systems.Convex formulation utilizing item metadata for accuracy-diversity trade-off.Design an efficient algorithm using split Bregman technique for our formulation. Design of
Autor:
Angshul Majumdar, Anupriya Gogna
Publikováno v:
Information Sciences. 378:59-74
Primary task of a recommender system is to improve user's experience by recommending relevant and interesting items to the users. To this effect, diversity in item suggestion is as important as the accuracy of recommendations. Existing literature aim
Autor:
Anupriya Gogna, Angshul Majumdar
Publikováno v:
Expert Systems with Applications. 42:5789-5799
Rating prediction accuracy of latent factor analysis based techniques in collaborative filtering is limited by the sparsity of available ratings. Usually more than 90% of the missing ratings need to be predicted from less than 10% of available rating
Autor:
Akash Tayal, Anupriya Gogna
Publikováno v:
Journal of Experimental & Theoretical Artificial Intelligence. 25:503-526
The area of metaheuristics has grown immensely in the past two decades as a solution to real-world optimisation problems. They are able to perform well in situations where exact optimisation techniques fail to deliver satisfactory results. For comple
Autor:
Anupriya Gogna, Angshul Majumdar
Publikováno v:
Neural Information Processing ISBN: 9783319466712
ICONIP (2)
ICONIP (2)
Autoencoders are self-supervised learning tools, but are unsupervised in the sense that class information is not required for training; but almost invariably they are used for supervised classification tasks. We propose to learn the autoencoder for a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b147988708dd7c689eba6c6c96a60e1
https://doi.org/10.1007/978-3-319-46672-9_10
https://doi.org/10.1007/978-3-319-46672-9_10
Autor:
Anupriya Gogna, Angshul Majumdar
Publikováno v:
Neural Information Processing ISBN: 9783319466712
ICONIP (2)
ICONIP (2)
The sparse representation based classification SRC was initially proposed for face recognition problems. However, SRC was found to excel in a variety of classification tasks. There have been many extensions to SRC, of which group SRC, kernel SRC bein
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::903c1b8274a9f7ba7d926deb29d83d44
https://doi.org/10.1007/978-3-319-46672-9_16
https://doi.org/10.1007/978-3-319-46672-9_16
Publikováno v:
Neural Information Processing ISBN: 9783319466804
ICONIP (4)
ICONIP (4)
A recent work introduced the concept of deep dictionary learning. The first level is a dictionary learning stage where the inputs are the training data and the outputs are the dictionary and learned coefficients. In subsequent levels of deep dictiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b659d835bf93da21444a6411c1130d58
https://doi.org/10.1007/978-3-319-46681-1_41
https://doi.org/10.1007/978-3-319-46681-1_41