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Approximation capability of neural networks on spaces of probability
  measures and tree-structured domains

Approximation capability of neural networks on spaces of probability measures and tree-structured domains

3 June 2019
Tomás Pevný
Vojtěch Kovařík
ArXivPDFHTML

Papers citing "Approximation capability of neural networks on spaces of probability measures and tree-structured domains"

6 / 6 papers shown
Title
How Smooth Is Attention?
How Smooth Is Attention?
Valérie Castin
Pierre Ablin
Gabriel Peyré
AAML
40
9
0
22 Dec 2023
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance
  Data
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data
Tomás Pevný
Viliam Lisý
B. Bosanský
P. Somol
Michal Pěchouček
17
1
0
04 Aug 2022
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction
  for learning from raw JSON data
Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data
Šimon Mandlík
Matej Racinsky
Viliam Lisý
Tomás Pevný
14
2
0
19 May 2021
Nested Multiple Instance Learning in Modelling of HTTP network traffic
Nested Multiple Instance Learning in Modelling of HTTP network traffic
Tomás Pevný
Marek Dedic
11
9
0
10 Feb 2020
Adversarial Attack and Defense on Point Sets
Adversarial Attack and Defense on Point Sets
Jiancheng Yang
Qiang Zhang
Rongyao Fang
Bingbing Ni
Jinxian Liu
Qi Tian
3DPC
24
122
0
28 Feb 2019
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional
  Filters
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Yifan Xu
Tianqi Fan
Mingye Xu
Long Zeng
Yu Qiao
3DV
3DPC
150
768
0
30 Mar 2018
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