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Montúfar, Guido
139  Ergebnisse:
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1

Algebraic optimization of sequential decision problems:

Dressler, Mareike ; Garrote-López, Marina ; Montúfar, Guido..
Journal of Symbolic Computation.  121 (2024)  - p. 102241 , 2024
Link: https://doi.org/10.1016/..
 
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2

Function Space and Critical Points of Linear Convolutional ..:

Kohn, Kathlén ; Montúfar, Guido ; Shahverdi, Vahid.
SIAM Journal on Applied Algebra and Geometry.  8 (2024)  2 - p. 333-362 , 2024
Link: https://doi.org/10.1137/..
 
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3

Geometry and convergence of natural policy gradient methods:

Müller, Johannes ; Montúfar, Guido
Information Geometry.  7 (2023)  S1 - p. 485-523 , 2023
Link: https://doi.org/10.1007/..
 
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4

Continuity and additivity properties of information decompo..:

Rauh, Johannes ; Banerjee, Pradeep Kr. ; Olbrich, Eckehard..
International Journal of Approximate Reasoning.  161 (2023)  - p. 108979 , 2023
Link: https://doi.org/10.1016/..
 
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5

Geometry of Linear Convolutional Networks:

Kohn, Kathlén ; Merkh, Thomas ; Montúfar, Guido.
SIAM Journal on Applied Algebra and Geometry.  6 (2022)  3 - p. 368-406 , 2022
Link: https://doi.org/10.1137/..
 
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6

Oversquashing in GNNs through the lens of information contr..:

, In: 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton),
Banerjee, Pradeep Kr. ; Karhadkar, Kedar ; Wang, Yu Guang.. - p. 1-8 , 2022
Link: https://doi.org/10.1109/..
 
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7

Sharp Bounds for the Number of Regions of Maxout Networks a..:

Montúfar, Guido ; Ren, Yue ; Zhang, Leon
SIAM Journal on Applied Algebra and Geometry.  6 (2022)  4 - p. 618-649 , 2022
Link: https://doi.org/10.1137/..
 
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8

Cell graph neural networks enable the precise prediction of..:

Wang, Yanan ; Wang, Yu Guang ; Hu, Changyuan...
npj Precision Oncology.  6 (2022)  1 - p. , 2022
Link: https://doi.org/10.1038/..
 
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9

Stochastic Feedforward Neural Networks: Universal Approxima..:

, In: Mathematical Aspects of Deep Learning,
Merkh, Thomas ; Montúfar, Guido - p. 267-313 , 2022
Link: https://doi.org/10.1017/..
 
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10

Wasserstein Proximal of GANs:

, In: Lecture Notes in Computer Science; Geometric Science of Information,
Lin, Alex Tong ; Li, Wuchen ; Osher, Stanley. - p. 524-533 , 2021
Link: https://doi.org/10.1007/..
 
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11

A Top-Down Approach to Attain Decentralized Multi-agents:

, In: Handbook of Reinforcement Learning and Control; Studies in Systems, Decision and Control,
Lin, Alex Tong ; Montúfar, Guido ; Osher, Stanley J. - p. 419-431 , 2021
Link: https://doi.org/10.1007/..
 
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12

Wasserstein distance to independence models:

Çelik, Türkü Özlüm ; Jamneshan, Asgar ; Montúfar, Guido..
Journal of Symbolic Computation.  104 (2021)  - p. 855-873 , 2021
Link: https://doi.org/10.1016/..
 
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13

Distributed Learning via Filtered Hyperinterpolation on Man..:

Montúfar, Guido ; Wang, Yu Guang
Foundations of Computational Mathematics.  22 (2021)  4 - p. 1219-1271 , 2021
Link: https://doi.org/10.1007/..
 
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14

Optimal Transport to a Variety:

, In: Mathematical Aspects of Computer and Information Sciences; Lecture Notes in Computer Science,
Çelik, Türkü Özlüm ; Jamneshan, Asgar ; Montúfar, Guido.. - p. 364-381 , 2020
Link: https://doi.org/10.1007/..
 
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15

The Variational Deficiency Bottleneck:

, In: 2020 International Joint Conference on Neural Networks (IJCNN),
Banerjee, Pradeep Kr. ; Montufar, Guido - p. 1-8 , 2020
Link: https://doi.org/10.1109/..
 
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