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1902.02767
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Hybrid Models with Deep and Invertible Features
7 February 2019
Eric T. Nalisnick
Akihiro Matsukawa
Yee Whye Teh
Dilan Görür
Balaji Lakshminarayanan
BDL
DRL
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Papers citing
"Hybrid Models with Deep and Invertible Features"
50 / 57 papers shown
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Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
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20 Feb 2024
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
Conference on Robot Learning (CoRL), 2023
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248
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MixerFlow: MLP-Mixer meets Normalising Flows
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25 Oct 2023
On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks
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271
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Fatih Porikli
323
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Wanqian Yang
Polina Kirichenko
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234
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270
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Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
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Yao Qiang
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235
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Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Data
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Zhe Huang
Mary-Joy Sidhom
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220
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Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model
Xiulong Yang
Sheng-Min Shih
Yinlin Fu
Xiaoting Zhao
Shihao Ji
DiffM
262
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16 Aug 2022
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
International Conference on Machine Learning (ICML), 2022
Julian Bitterwolf
Alexander Meinke
Maximilian Augustin
Matthias Hein
OODD
241
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20 Jun 2022
Universal approximation property of invertible neural networks
Journal of machine learning research (JMLR), 2022
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Takeshi Teshima
Koichi Tojo
Kenta Oono
Masahiro Ikeda
Masashi Sugiyama
219
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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
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Andy Jones
Kamal Ndousse
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Anna Chen
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Jack Clark
Sam McCandlish
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Jared Kaplan
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12 Apr 2022
Deep Generative Models for Vehicle Speed Trajectories
F. Behnia
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Vadim Sokolov
161
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Autoregressive Quantile Flows for Predictive Uncertainty Estimation
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Allan Bishop
Volodymyr Kuleshov
BDL
UQCV
AI4TS
441
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A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Jihyo Kim
Jiin Koo
Sangheum Hwang
UQCV
319
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01 Dec 2021
Types of Out-of-Distribution Texts and How to Detect Them
Udit Arora
William Huang
He He
OODD
491
113
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14 Sep 2021
Machine Learning with a Reject Option: A survey
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Kilian Hendrickx
Lorenzo Perini
Dries Van der Plas
Wannes Meert
Jesse Davis
MU
333
160
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23 Jul 2021
Featurized Density Ratio Estimation
Kristy Choi
Madeline Liao
Stefano Ermon
TPM
159
33
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05 Jul 2021
On the Practicality of Deterministic Epistemic Uncertainty
Janis Postels
Mattia Segu
Tao Sun
Luca Sieber
Luc Van Gool
Feng Yu
Federico Tombari
UQCV
389
71
0
01 Jul 2021
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Polina Kirichenko
Mehrdad Farajtabar
Dushyant Rao
Balaji Lakshminarayanan
Nir Levine
Ang Li
Huiyi Hu
A. Wilson
Razvan Pascanu
VLM
BDL
CLL
140
23
0
24 Jun 2021
Densely connected normalizing flows
Neural Information Processing Systems (NeurIPS), 2021
Matej Grcić
Ivan Grubišić
Sinisa Segvic
TPM
339
64
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08 Jun 2021
Exploring the Limits of Out-of-Distribution Detection
Neural Information Processing Systems (NeurIPS), 2021
Stanislav Fort
Jie Jessie Ren
Balaji Lakshminarayanan
587
398
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06 Jun 2021
InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
Qili Zeng
Shihang Feng
B. Wohlberg
Youzuo Lin
285
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25 Mar 2021
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
Takuya Kurihana
Elisabeth Moyer
Rebecca Willett
Davis Gilton
Ian Foster
107
19
0
08 Mar 2021
Invertible DenseNets with Concatenated LipSwish
Neural Information Processing Systems (NeurIPS), 2021
Yura Perugachi-Diaz
Jakub M. Tomczak
Sandjai Bhulai
365
25
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04 Feb 2021
On Batch Normalisation for Approximate Bayesian Inference
Jishnu Mukhoti
P. Dokania
Juil Sock
Y. Gal
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125
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24 Dec 2020
Further Analysis of Outlier Detection with Deep Generative Models
Neural Information Processing Systems (NeurIPS), 2020
Ziyu Wang
Bin Dai
David Wipf
Jun Zhu
228
41
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Generative Classifiers as a Basis for Trustworthy Image Classification
Radek Mackowiak
Lynton Ardizzone
Ullrich Kothe
Carsten Rother
222
4
0
29 Jul 2020
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens
Rudy Bunel
Abhijit Guha Roy
Robert Stanforth
Vivek Natarajan
...
Alan Karthikesalingam
Simon A. A. Kohl
taylan. cemgil
S. M. Ali Eslami
Olaf Ronneberger
OODD
390
259
0
10 Jul 2020
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
Ivana Balazevic
Carl Allen
Timothy M. Hospedales
96
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06 Jul 2020
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Takeshi Teshima
Isao Ishikawa
Koichi Tojo
Kenta Oono
Masahiro Ikeda
Masashi Sugiyama
266
118
0
20 Jun 2020
Ordering Dimensions with Nested Dropout Normalizing Flows
Artur Bekasov
Iain Murray
DRL
156
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Depth Uncertainty in Neural Networks
Javier Antorán
J. Allingham
José Miguel Hernández-Lobato
UQCV
OOD
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412
113
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Revisiting Explicit Regularization in Neural Networks for Well-Calibrated Predictive Uncertainty
Taejong Joo
U. Chung
BDL
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258
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GAN-based Priors for Quantifying Uncertainty
Dhruv V. Patel
Assad A. Oberai
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114
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Hybrid Models for Open Set Recognition
European Conference on Computer Vision (ECCV), 2020
Hongjie Zhang
Ang Li
Jie Guo
Yanwen Guo
BDL
429
205
0
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Uncertainty Estimation Using a Single Deep Deterministic Neural Network
International Conference on Machine Learning (ICML), 2020
Joost R. van Amersfoort
Lewis Smith
Yee Whye Teh
Y. Gal
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277
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VFlow: More Expressive Generative Flows with Variational Data Augmentation
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Cheng Lu
Biqi Chenli
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Tian Tian
DRL
199
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0
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Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
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Laurent Dinh
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306
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Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
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Radek Mackowiak
Ullrich Kothe
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357
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Semi-Supervised Learning with Normalizing Flows
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Pavel Izmailov
Polina Kirichenko
Marc Finzi
A. Wilson
DRL
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163
131
0
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InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
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Richard G. Baraniuk
Anima Anandkumar
TPM
191
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Normalizing Flows for Probabilistic Modeling and Inference
Journal of machine learning research (JMLR), 2019
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Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
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2,083
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F. Falasca
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178
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