東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

From unimodal to multimodal machine learning = an overview /

Record Type:	Electronic resources : Monograph/item
Title/Author:	From unimodal to multimodal machine learning/ by Blaž Škrlj.
Reminder of title:	an overview /
Author:	Škrlj, Blaž.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xiii, 70 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Part.I. Introduction -- Chapter.1.A brief overview of machine learning -- Chapter.2.Data modalities and representation learning -- Part II Unimodal machine learning -- Chapter.3.Learning from text -- Chapter.4.Graph-based methods -- Chapter.5 Computer vision -- Part. III. Multimodal machine learning -- Chapter.6.Multimodal learning -- Part. IV.A look forward -- Chapter.7.Future prospects.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-57016-2
ISBN:	9783031570162

From unimodal to multimodal machine learning = an overview /
Škrlj, Blaž.

From unimodal to multimodal machine learningan overview /[electronic resource] :by Blaž Škrlj. - Cham :Springer Nature Switzerland :2024. - xiii, 70 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Part.I. Introduction -- Chapter.1.A brief overview of machine learning -- Chapter.2.Data modalities and representation learning -- Part II Unimodal machine learning -- Chapter.3.Learning from text -- Chapter.4.Graph-based methods -- Chapter.5 Computer vision -- Part. III. Multimodal machine learning -- Chapter.6.Multimodal learning -- Part. IV.A look forward -- Chapter.7.Future prospects.

With the increasing amount of various data types, machine learning methods capable of leveraging diverse sources of information have become highly relevant. Deep learning-based approaches have made significant progress in learning from texts and images in recent years. These methods enable simultaneous learning from different types of representations (embeddings) Substantial advancements have also been made in joint learning from different types of spaces. Additionally, other modalities such as sound, physical signals from the environment, and time series-based data have been recently explored. Multimodal machine learning, which involves processing and learning from data across multiple modalities, has opened up new possibilities in a wide range of applications, including speech recognition, natural language processing, and image recognition. From Unimodal to Multimodal Machine Learning: An Overview gradually introduces the concept of multimodal machine learning, providing readers with the necessary background to understand this type of learning and its implications. Key methods representative of different modalities are described in more detail, aiming to offer an understanding of the peculiarities of various types of data and how multimodal approaches tend to address them (although not yet in some cases) The book examines the implications of multimodal learning in other domains and presents alternative approaches that offer computationally simpler yet still applicable solutions. The final part of the book focuses on intriguing open research problems, making it useful for practitioners who wish to better understand the limitations of existing methods and explore potential research avenues to overcome them.

ISBN: 9783031570162

Standard No.: 10.1007/978-3-031-57016-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

From unimodal to multimodal machine learning = an overview /
LDR:03172nmm a2200337 a 4500 001 2371231
003 DE-He213
005 20240522102438.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031570162 $q (electronic bk.)
020 $a 9783031570155 $q (paper)
024 7 $a 10.1007/978-3-031-57016-2 $2 doi
035 $a 978-3-031-57016-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .S629 2024
100 1 $a Škrlj, Blaž. $3 3717092
245 1 0 $a From unimodal to multimodal machine learning $h [electronic resource] : $b an overview / $c by Blaž Škrlj.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xiii, 70 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5776
505 0 $a Part.I. Introduction -- Chapter.1.A brief overview of machine learning -- Chapter.2.Data modalities and representation learning -- Part II Unimodal machine learning -- Chapter.3.Learning from text -- Chapter.4.Graph-based methods -- Chapter.5 Computer vision -- Part. III. Multimodal machine learning -- Chapter.6.Multimodal learning -- Part. IV.A look forward -- Chapter.7.Future prospects.
520 $a With the increasing amount of various data types, machine learning methods capable of leveraging diverse sources of information have become highly relevant. Deep learning-based approaches have made significant progress in learning from texts and images in recent years. These methods enable simultaneous learning from different types of representations (embeddings) Substantial advancements have also been made in joint learning from different types of spaces. Additionally, other modalities such as sound, physical signals from the environment, and time series-based data have been recently explored. Multimodal machine learning, which involves processing and learning from data across multiple modalities, has opened up new possibilities in a wide range of applications, including speech recognition, natural language processing, and image recognition. From Unimodal to Multimodal Machine Learning: An Overview gradually introduces the concept of multimodal machine learning, providing readers with the necessary background to understand this type of learning and its implications. Key methods representative of different modalities are described in more detail, aiming to offer an understanding of the peculiarities of various types of data and how multimodal approaches tend to address them (although not yet in some cases) The book examines the implications of multimodal learning in other domains and presents alternative approaches that offer computationally simpler yet still applicable solutions. The final part of the book focuses on intriguing open research problems, making it useful for practitioners who wish to better understand the limitations of existing methods and explore potential research avenues to overcome them.
650 0 $a Machine learning. $3 533906
650 2 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in computer science. $3 1567571
856 4 0 $u https://doi.org/10.1007/978-3-031-57016-2
950 $a Computer Science (SpringerNature-11645)