ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1906.04370
  4. Cited By
Maximum Mean Discrepancy Gradient Flow

Maximum Mean Discrepancy Gradient Flow

11 June 2019
Michael Arbel
Anna Korba
Adil Salim
A. Gretton
ArXivPDFHTML

Papers citing "Maximum Mean Discrepancy Gradient Flow"

30 / 30 papers shown
Title
A Dictionary of Closed-Form Kernel Mean Embeddings
A Dictionary of Closed-Form Kernel Mean Embeddings
F. Briol
A. Gessner
Toni Karvonen
Maren Mahsereci
BDL
78
1
0
26 Apr 2025
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Raphael Barboni
Gabriel Peyré
François-Xavier Vialard
MLT
34
0
0
25 Apr 2025
Flow Matching Ergodic Coverage
Flow Matching Ergodic Coverage
Max Muchen Sun
Allison Pinosky
Todd Murphey
42
0
0
24 Apr 2025
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
Jonathan Geuter
Clément Bonet
Anna Korba
David Alvarez-Melis
61
0
0
03 Mar 2025
Non-geodesically-convex optimization in the Wasserstein space
Non-geodesically-convex optimization in the Wasserstein space
Hoang Phuc Hau Luu
Hanlin Yu
Bernardo Williams
Petrus Mikkola
Marcelo Hartmann
Kai Puolamaki
Arto Klami
53
2
0
08 Jan 2025
Fast Summation of Radial Kernels via QMC Slicing
Fast Summation of Radial Kernels via QMC Slicing
Johannes Hertrich
Tim Jahn
Michael Quellmalz
26
5
0
02 Oct 2024
Particle Semi-Implicit Variational Inference
Particle Semi-Implicit Variational Inference
Jen Ning Lim
A. M. Johansen
48
3
0
30 Jun 2024
Deep MMD Gradient Flow without adversarial training
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov
Valentin De Bortoli
Arthur Gretton
DiffM
40
8
0
10 May 2024
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational
  Inference Framework
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework
Fangyikang Wang
Huminhao Zhu
Chao Zhang
Han Zhao
Hui Qian
24
5
0
27 Dec 2023
Mean-field underdamped Langevin dynamics and its spacetime
  discretization
Mean-field underdamped Langevin dynamics and its spacetime discretization
Qiang Fu
Ashia Wilson
40
4
0
26 Dec 2023
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Ilana Sebag
Muni Sreenivas Pydi
Jean-Yves Franceschi
Alain Rakotomamonjy
Mike Gartrell
Jamal Atif
Alexandre Allauzen
24
2
0
13 Dec 2023
Estimation Beyond Data Reweighting: Kernel Method of Moments
Estimation Beyond Data Reweighting: Kernel Method of Moments
Heiner Kremer
Yassine Nemmour
Bernhard Schölkopf
Jia-Jie Zhu
36
7
0
18 May 2023
The Score-Difference Flow for Implicit Generative Modeling
The Score-Difference Flow for Implicit Generative Modeling
Romann M. Weber
DiffM
29
2
0
25 Apr 2023
Birth-death dynamics for sampling: Global convergence, approximations
  and their asymptotics
Birth-death dynamics for sampling: Global convergence, approximations and their asymptotics
Yulong Lu
D. Slepčev
Lihan Wang
37
22
0
01 Nov 2022
Block-wise Training of Residual Networks via the Minimizing Movement
  Scheme
Block-wise Training of Residual Networks via the Minimizing Movement Scheme
Skander Karkar
Ibrahim Ayed
Emmanuel de Bézenac
Patrick Gallinari
30
1
0
03 Oct 2022
Membership Inference Attacks via Adversarial Examples
Membership Inference Attacks via Adversarial Examples
Hamid Jalalzai
Elie Kadoche
Rémi Leluc
Vincent Plassier
AAML
FedML
MIACV
38
7
0
27 Jul 2022
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
Yindong Chen
Yiwei Wang
Lulu Kang
Chun Liu
21
1
0
21 Nov 2021
Efficient Gradient Flows in Sliced-Wasserstein Space
Efficient Gradient Flows in Sliced-Wasserstein Space
Clément Bonet
Nicolas Courty
Franccois Septier
Lucas Drumetz
34
21
0
21 Oct 2021
Dual Training of Energy-Based Models with Overparametrized Shallow
  Neural Networks
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich
A. Bietti
Marylou Gabrié
Joan Bruna
Eric Vanden-Eijnden
FedML
32
6
0
11 Jul 2021
Generalization Error of GAN from the Discriminator's Perspective
Generalization Error of GAN from the Discriminator's Perspective
Hongkang Yang
Weinan E
GAN
40
13
0
08 Jul 2021
Deep Generative Learning via Schrödinger Bridge
Deep Generative Learning via Schrödinger Bridge
Gefei Wang
Yuling Jiao
Qiang Xu
Yang Wang
Can Yang
DiffM
OT
23
92
0
19 Jun 2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint
  Support
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
Pierre Glaser
Michael Arbel
A. Gretton
46
37
0
16 Jun 2021
A Neural Tangent Kernel Perspective of GANs
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi
Emmanuel de Bézenac
Ibrahim Ayed
Mickaël Chen
Sylvain Lamprier
Patrick Gallinari
31
26
0
10 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex
  Neural Networks
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
David Alvarez-Melis
Yair Schiff
Youssef Mroueh
40
53
0
01 Jun 2021
Kernel Stein Discrepancy Descent
Kernel Stein Discrepancy Descent
Anna Korba
Pierre-Cyril Aubin-Frankowski
Szymon Majewski
Pierre Ablin
19
50
0
20 May 2021
Stein Variational Gradient Descent: many-particle and long-time
  asymptotics
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Nikolas Nusken
D. M. Renger
27
22
0
25 Feb 2021
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient
  Flow
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Youssef Mroueh
Truyen V. Nguyen
29
25
0
04 Nov 2020
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean
  field training perspective
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective
Stephan Wojtowytsch
E. Weinan
MLT
26
48
0
21 May 2020
Statistical and Topological Properties of Sliced Probability Divergences
Statistical and Topological Properties of Sliced Probability Divergences
Kimia Nadjahi
Alain Durmus
Lénaïc Chizat
Soheil Kolouri
Shahin Shahrampour
Umut Simsekli
26
81
0
12 Mar 2020
Machine Learning from a Continuous Viewpoint
Machine Learning from a Continuous Viewpoint
E. Weinan
Chao Ma
Lei Wu
23
102
0
30 Dec 2019
1