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Incremental Sampling Without Replacement for Sequence Models
v1v2 (latest)

Incremental Sampling Without Replacement for Sequence Models

International Conference on Machine Learning (ICML), 2020
21 February 2020
Kensen Shi
David Bieber
Charles Sutton
    VLMSyDaBDL
ArXiv (abs)PDFHTML

Papers citing "Incremental Sampling Without Replacement for Sequence Models"

11 / 11 papers shown
Title
GraphXForm: Graph transformer for computer-aided molecular design
GraphXForm: Graph transformer for computer-aided molecular designDigital Discovery (DD), 2024
Jonathan Pirnay
Jan G. Rittig
Alexander B. Wolf
Martin Grohe
Jakob Burger
Alexander Mitsos
D. G. Grimm
AI4CE
260
4
0
03 Nov 2024
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural
  Combinatorial Optimization
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization
Jonathan Pirnay
D. G. Grimm
BDL
170
4
0
24 Jul 2024
Towards Model-Agnostic Posterior Approximation for Fast and Accurate
  Variational Autoencoders
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez
DRL
247
0
0
13 Mar 2024
LambdaBeam: Neural Program Search with Higher-Order Functions and
  Lambdas
LambdaBeam: Neural Program Search with Higher-Order Functions and LambdasNeural Information Processing Systems (NeurIPS), 2023
Kensen Shi
H. Dai
Wen-Ding Li
Kevin Ellis
Charles Sutton
144
8
0
03 Jun 2023
Predictive Querying for Autoregressive Neural Sequence Models
Predictive Querying for Autoregressive Neural Sequence ModelsNeural Information Processing Systems (NeurIPS), 2022
Alex Boyd
Samuel Showalter
Stephan Mandt
Padhraic Smyth
BDLAI4TS
193
4
0
12 Oct 2022
Unbiased and Efficient Sampling of Dependency Trees
Unbiased and Efficient Sampling of Dependency TreesConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Milovs Stanojević
139
3
0
25 May 2022
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
CrossBeam: Learning to Search in Bottom-Up Program SynthesisInternational Conference on Learning Representations (ICLR), 2022
Kensen Shi
H. Dai
Kevin Ellis
Charles Sutton
NAI
71
27
0
20 Mar 2022
A Review of the Gumbel-max Trick and its Extensions for Discrete
  Stochasticity in Machine Learning
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Iris A. M. Huijben
W. Kool
Max B. Paulus
Ruud J. G. van Sloun
224
117
0
04 Oct 2021
Conditional Poisson Stochastic Beam Search
Conditional Poisson Stochastic Beam SearchConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Clara Meister
Afra Amini
Tim Vieira
Robert Bamler
133
12
0
22 Sep 2021
Determinantal Beam Search
Determinantal Beam SearchAnnual Meeting of the Association for Computational Linguistics (ACL), 2021
Clara Meister
Martina Forster
Robert Bamler
153
16
0
14 Jun 2021
Low-Variance Policy Gradient Estimation with World Models
Low-Variance Policy Gradient Estimation with World Models
Michal Nauman
Floris den Hengst
OffRL
96
1
0
29 Oct 2020
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