Forre patrick (12 résultats)
Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks (Progress in Data Science)
Weiler, Maurice; Forré, Patrick; Verlinde, Erik; Welling, Max
- Couverture rigide
Vendeur : BOOKWEST, Phoenix, AZ, Etats-UnisBOOKWEST
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Hardcover. Etat : New. US SELLER SHIPS FROM USA.
- Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
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EUR 185,29
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Etat : New.
Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks (Progress in Data Science)
Weiler, Maurice; Forré, Patrick; Verlinde, Erik; Welling, Max
- Couverture rigide
Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 187,65
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Etat : New.
- Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-UnisGreatBookPrices
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EUR 198,35
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- Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
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EUR 186,84
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Etat : New.
Langue : anglais
Edité par World Scientific Publishing Co Pte Ltd, SG, 2026
- Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
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EUR 218,83
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Hardback. Etat : New. What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interested in are fi…elds of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of "Equivariant and Coordinate Independent CNNs".
- Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-UniGreatBookPricesUK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Occasion - Comme neuf
EUR 204,79
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Etat : As New. Unread book in perfect condition.
Langue : anglais
Edité par World Scientific Publishing Co Pte Ltd, Singapore, 2026
- Couverture rigide
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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EUR 229,64
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Hardcover. Etat : new. Hardcover. What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interest…ed in are fields of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of "Equivariant and Coordinate Independent CNNs". Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks: 1 (Progress In Data Science)
Forre, Patrick (Author)/ Verlinde, Erik (Author)/ Weiler, Maurice (Author)/ Welling, Max (Author)
- Couverture rigide
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 249,55
EUR 14,61 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 2 disponible(s)
Hardcover. Etat : Brand New. 592 pages. 2.36x0.85x9.00 inches. In Stock.
Langue : anglais
Edité par World Scientific Publishing Co Pte Ltd, SG, 2026
- Couverture rigide
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 214,48
EUR 75,96 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 15 disponible(s)
Hardback. Etat : New. What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interested in are fi…elds of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of "Equivariant and Coordinate Independent CNNs".
Langue : anglais
Edité par World Scientific Publishing Co Pte Ltd, Singapore, 2026
- Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 346,26
EUR 31,95 expéditionExpédition depuis Australie vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interest…ed in are fields of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of 'Equivariant and Coordinate Independent CNNs'. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Langue : anglais
Edité par World Scientific Publishing Co Pte Ltd, Singapore, 2026
- Couverture rigide
- impression à la demande
Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 223,26
EUR 43,24 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. What is the appropriate geometric structure for neural networks that process spatial signals on Euclidean spaces or more general manifolds? This question takes us on a journey which leads to a gauge field theory of convolutional networks.Feature vector fields: The spatial signals we are interest…ed in are fields of feature vectors. Feature fields allow to describe data like images, audio, videos, point clouds, or tensor fields, such as fluid flows and electromagnetic fields.Equivariant networks commute with actions of some symmetry group on their feature spaces. The relevant group actions in this work are geometric transformations of feature fields, like translations, rotations, or reflections of images. Equivariant models generalize everything they learn over the considered group of transformations. This property makes them significantly more data efficient, interpretable, and robust in comparison to non-equivariant models.Convolutional Neural Networks (CNNs) are the most common network architecture for processing feature fields. Conventional CNNs operate on Euclidean spaces and are translation equivariant, i.e. position independent. This work explains how to extend CNNs to be equivariant under more general symmetries of space.Coordinate independence: Manifolds are in general not equipped with a canonical choice of coordinates. Feature fields and neural network layers are hence required to be coordinate independent, that is, expressible relative to different frames of reference. The ambiguity of local frames represents the gauge freedom of our neural field theory. We show that the demand for coordinate independence requires CNNs to be equivariant under local gauge transformations.To offer an easy entry, the first part of this work focuses on the representation theory of equivariant convolutional networks on Euclidean spaces. The insights gained in the Euclidean setting are subsequently leveraged to develop the full gauge theory of coordinate independent CNNs on Riemannian manifolds. In the last part, we turn to a discussion of practical applications on specific manifolds. A comprehensive literature review demonstrates the generality of our theory by showing for more than 100 models from the literature how they can be understood as specific instantiations of 'Equivariant and Coordinate Independent CNNs'. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
