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1705.11140
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Variational Sequential Monte Carlo
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
31 May 2017
C. A. Naesseth
Scott W. Linderman
Rajesh Ranganath
David M. Blei
BDL
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Papers citing
"Variational Sequential Monte Carlo"
50 / 147 papers shown
SIXO: Smoothing Inference with Twisted Objectives
Dieterich Lawson
Allan Raventós
Andrew Warrington
Scott W. Linderman
BDL
439
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0
13 Jun 2022
Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces
Dawei Chen
Samuel Yang-Zhao
John Lloyd
K. S. Ng
AI4TS
184
1
0
05 Jun 2022
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Alexander K. Lew
Marco F. Cusumano-Towner
Vikash K. Mansinghka
BDL
240
12
0
05 Mar 2022
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Journal of machine learning research (JMLR), 2022
Adrien Corenflos
Nicolas Chopin
Simo Särkkä
143
8
0
04 Feb 2022
Continual Repeated Annealed Flow Transport Monte Carlo
International Conference on Machine Learning (ICML), 2022
A. G. Matthews
Michael Arbel
Danilo Jimenez Rezende
Arnaud Doucet
OT
377
66
0
31 Jan 2022
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
Pierre-Alexandre Mattei
J. Frellsen
144
4
0
26 Jan 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling
International Conference on Machine Learning (ICML), 2021
M. Jankowiak
Du Phan
BDL
245
14
0
22 Dec 2021
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
Giseung Park
Sungho Choi
Y. Sung
OffRL
194
3
0
10 Dec 2021
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
Shivendra Agrawal
Hwanwoo Kim
D. Sanz-Alonso
A. Strang
166
13
0
26 Nov 2021
Online Variational Filtering and Parameter Learning
Andrew Campbell
Yuyang Shi
Tom Rainforth
Arnaud Doucet
BDL
281
25
0
26 Oct 2021
Unsupervised Learned Kalman Filtering
Guy Revach
Stefano Rini
Timur Locher
Xiaoyong Ni
Ruud J. G. van Sloun
Yonina C. Eldar
SSL
212
41
0
18 Oct 2021
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021
Itzik Klein
Guy Revach
Stefano Rini
Jonas E. Mehr
Ruud J. G. van Sloun
Yonina C. Eldar
161
15
0
10 Oct 2021
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Xin Huang
Guy Rosman
Igor Gilitschenski
A. Jasour
Stephen G. McGill
J. Leonard
B. Williams
205
27
0
05 Oct 2021
Variational Marginal Particle Filters
Jinlin Lai
Justin Domke
Daniel Sheldon
231
13
0
30 Sep 2021
Self-Supervised Inference in State-Space Models
International Conference on Learning Representations (ICLR), 2021
David Ruhe
Patrick Forré
BDL
427
7
0
28 Jul 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Neural Information Processing Systems (NeurIPS), 2021
Guodong Zhang
Kyle Hsu
Jianing Li
Chelsea Finn
Roger C. Grosse
270
43
0
21 Jul 2021
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
IEEE Transactions on Signal Processing (IEEE TSP), 2021
Guy Revach
Stefano Rini
Xiaoyong Ni
Adrià López Escoriza
Ruud J. G. van Sloun
Yonina C. Eldar
357
426
0
21 Jul 2021
Structured World Belief for Reinforcement Learning in POMDP
International Conference on Machine Learning (ICML), 2021
Gautam Singh
Skand Peri
Junghyun Kim
Hyunseok Kim
Sungjin Ahn
OCL
204
33
0
19 Jul 2021
Auto-differentiable Ensemble Kalman Filters
SIAM Journal on Mathematics of Data Science (SIMODS), 2021
Yuming Chen
D. Sanz-Alonso
Rebecca Willett
303
40
0
16 Jul 2021
On Incorporating Inductive Biases into VAEs
International Conference on Learning Representations (ICLR), 2021
Ning Miao
Emile Mathieu
N. Siddharth
Yee Whye Teh
Tom Rainforth
CML
DRL
257
11
0
25 Jun 2021
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
Armand Jordana
Justin Carpentier
Ludovic Righetti
AI4CE
148
5
0
22 Jun 2021
Nested Variational Inference
Neural Information Processing Systems (NeurIPS), 2021
Heiko Zimmermann
Hao Wu
Babak Esmaeili
Jan-Willem van de Meent
BDL
164
23
0
21 Jun 2021
Differentiable Particle Filtering without Modifying the Forward Pass
Adam Scibior
Frank Wood
210
25
0
18 Jun 2021
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
International Conference on Machine Learning (ICML), 2021
Soumyasundar Pal
Liheng Ma
Yingxue Zhang
Mark Coates
BDL
AI4TS
176
26
0
10 Jun 2021
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
A. Moretti
Liyi Zhang
C. A. Naesseth
Hadiah K Venner
David M. Blei
I. Pe’er
BDL
265
17
0
31 May 2021
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series
International Joint Conference on Artificial Intelligence (IJCAI), 2021
Shuangshuang Chen
Sihao Ding
Y. Karayiannidis
Mårten Björkman
BDL
AI4TS
112
2
0
20 May 2021
Variational Rejection Particle Filtering
Rahul Sharma
S. Banerjee
Dootika Vats
Piyush Rai
BDL
175
0
0
29 Mar 2021
Learning Proposals for Probabilistic Programs with Inference Combinators
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Sam Stites
Heiko Zimmermann
Hao Wu
Eli Sennesh
Jan-Willem van de Meent
NAI
292
18
0
01 Mar 2021
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations
Justin Domke
98
11
0
25 Feb 2021
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Foundations of Data Science (FODS), 2021
Nikolas Nusken
D. M. Renger
219
25
0
25 Feb 2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
International Conference on Machine Learning (ICML), 2021
Yangjun Ruan
Karen Ullrich
Daniel de Souza Severo
James Townsend
Ashish Khisti
Arnaud Doucet
Alireza Makhzani
Chris J. Maddison
238
25
0
22 Feb 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
International Conference on Machine Learning (ICML), 2021
Adrien Corenflos
James Thornton
George Deligiannidis
Arnaud Doucet
OT
230
87
0
15 Feb 2021
Annealed Flow Transport Monte Carlo
International Conference on Machine Learning (ICML), 2021
Michael Arbel
A. G. Matthews
Arnaud Doucet
315
94
0
15 Feb 2021
How to Train Your Differentiable Filter
Autonomous Robots (Auton. Robots), 2020
Alina Kloss
Georg Martius
Jeannette Bohg
399
53
0
28 Dec 2020
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization
Conference on Uncertainty in Artificial Intelligence (UAI), 2020
Nicola Branchini
Victor Elvira
417
25
0
18 Nov 2020
End-To-End Semi-supervised Learning for Differentiable Particle Filters
Hao Wen
Xiongjie Chen
Georgios Papagiannis
Conghui Hu
Yunpeng Li
176
17
0
11 Nov 2020
Variational Dynamic Mixtures
Chen Qiu
Stephan Mandt
Maja R. Rudolph
BDL
AI4TS
313
2
0
20 Oct 2020
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter
Tsuyoshi Ishizone
T. Higuchi
Kazuyuki Nakamura
BDL
186
1
0
17 Oct 2020
MCMC-Interactive Variational Inference
Quan Zhang
Huangjie Zheng
Mingyuan Zhou
188
1
0
02 Oct 2020
Variational Filtering with Copula Models for SLAM
IEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020
John D. Martin
Kevin Doherty
Caralyn Cyr
Brendan Englot
J. Leonard
137
3
0
02 Aug 2020
An invitation to sequential Monte Carlo samplers
Chenguang Dai
J. Heng
Pierre E. Jacob
N. Whiteley
423
79
0
23 Jul 2020
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
Alice Martin
Charles Ollion
Florian Strub
Sylvain Le Corff
Olivier Pietquin
180
7
0
15 Jul 2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Abhinav Agrawal
Daniel Sheldon
Justin Domke
TPM
BDL
211
44
0
18 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data
Farzana Jahan
Insha Ullah
Kerrie Mengersen
279
16
0
08 Jun 2020
Markovian Score Climbing: Variational Inference with KL(p||q)
Neural Information Processing Systems (NeurIPS), 2020
C. A. Naesseth
Fredrik Lindsten
David M. Blei
304
59
0
23 Mar 2020
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
International Conference on Learning Representations (ICLR), 2020
Xiao Ma
Peter Karkus
David Hsu
W. Lee
N. Ye
OffRL
155
48
0
23 Feb 2020
Deep Variational Luenberger-type Observer for Stochastic Video Prediction
Dong Wang
Feng Zhou
Zheng Yan
Guang Yao
Zongxuan Liu
Wennan Ma
Cewu Lu
148
0
0
12 Feb 2020
Relational State-Space Model for Stochastic Multi-Object Systems
International Conference on Learning Representations (ICLR), 2020
Fan Yang
Ling Chen
Fan Zhou
Yusong Gao
Wei Cao
254
8
0
13 Jan 2020
Lazy object copy as a platform for population-based probabilistic programming
Lawrence M. Murray
141
5
0
09 Jan 2020
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
International Conference on Machine Learning (ICML), 2019
Hao Wu
Heiko Zimmermann
Eli Sennesh
T. Le
Jan-Willem van de Meent
210
7
0
04 Nov 2019
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