Neural Processes

4 July 2018

M. Garnelo

Jonathan Richard Schwarz

Dan Rosenbaum

Fabio Viola

Danilo Jimenez Rezende

Papers citing "Neural Processes"

46 / 96 papers shown

Title
Continual Learning of Multi-modal Dynamics with External Memory Abdullah Akgul Gözde B. Ünal M. Kandemir CLL 19 0 0 02 Mar 2022
Transformers Can Do Bayesian Inference Samuel G. Müller Noah Hollmann Sebastian Pineda Arango Josif Grabocka Frank Hutter BDL UQCV 19 139 0 20 Dec 2021
A Probabilistic Hard Attention Model For Sequentially Observed Scenes Samrudhdhi B. Rangrej James J. Clark 24 12 0 15 Nov 2021
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 16 8 0 10 Nov 2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators Aviral Kumar Amir Yazdanbakhsh Milad Hashemi Kevin Swersky Sergey Levine 25 36 0 20 Oct 2021
How Neural Processes Improve Graph Link Prediction Huidong Liang Junbin Gao AI4CE 33 13 0 30 Sep 2021
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning Zhe Wang J. E. Grigsby Arshdeep Sekhon Yanjun Qi 47 4 0 27 Sep 2021
Conservative Objective Models for Effective Offline Model-Based Optimization Brandon Trabucco Aviral Kumar Xinyang Geng Sergey Levine OffRL 33 86 0 14 Jul 2021
Meta-learning Amidst Heterogeneity and Ambiguity Kyeongryeol Go Seyoung Yun 24 1 0 05 Jul 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 17 19 0 21 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster Desi R. Ivanova Ilyas Malik Tom Rainforth 26 78 0 03 Mar 2021
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments Baiming Chen Zuxin Liu Jiacheng Zhu Mengdi Xu Wenhao Ding Ding Zhao 17 35 0 02 Jan 2021
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes P. Holderrieth M. Hutchinson Yee Whye Teh BDL 28 30 0 25 Nov 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang Lars Lorch Moritz Graule Himabindu Lakkaraju Finale Doshi-Velez UQCV BDL 17 16 0 21 Oct 2020
Few-shot Learning for Spatial Regression Tomoharu Iwata Yusuke Tanaka 18 11 0 09 Oct 2020
Uncertainty in Neural Processes Saeid Naderiparizi Ke-Li Chiu Benjamin Bloem-Reddy Frank D. Wood UQCV BDL AI4CE 11 4 0 08 Oct 2020
S2RMs: Spatially Structured Recurrent Modules Nasim Rahaman Anirudh Goyal Muhammad Waleed Gondal M. Wuthrich Stefan Bauer Yash Sharma Yoshua Bengio Bernhard Schölkopf 21 14 0 13 Jul 2020
NP-PROV: Neural Processes with Position-Relevant-Only Variances Xuesong Wang Lina Yao Xianzhi Wang Feiping Nie BDL 21 3 0 15 Jun 2020
Energy-Based Processes for Exchangeable Data Mengjiao Yang Bo Dai H. Dai Dale Schuurmans 17 12 0 17 Mar 2020
Model Inversion Networks for Model-Based Optimization Aviral Kumar Sergey Levine OffRL 22 93 0 31 Dec 2019
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing V. Monga Yuelong Li Yonina C. Eldar 28 997 0 22 Dec 2019
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO Pablo Morales-Álvarez Pablo Ruiz S. Coughlin Rafael Molina Aggelos K. Katsaggelos 18 14 0 05 Nov 2019
Convolutional Conditional Neural Processes Jonathan Gordon W. Bruinsma Andrew Y. K. Foong James Requeima Yann Dubois Richard E. Turner BDL 19 162 0 29 Oct 2019
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process Jiacheng Zhu Shenghao Qin Wenshuo Wang Ding Zhao 17 10 0 17 Oct 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 19 38 0 17 Oct 2019
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference Cuong C. Nguyen Thanh-Toan Do G. Carneiro OOD BDL UQCV 19 54 0 27 Jul 2019
GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin Dmitry Baranchuk Gunnar Rätsch Stephan Mandt BDL AI4TS 17 245 0 09 Jul 2019
Sequential Neural Processes Gautam Singh Jaesik Yoon Youngsung Son Sungjin Ahn BDL AI4TS 32 81 0 24 Jun 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 23 77 0 19 Jun 2019
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes James Requeima Jonathan Gordon J. Bronskill Sebastian Nowozin Richard E. Turner 24 240 0 18 Jun 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak J. Gardner UQCV BDL 27 3 0 31 May 2019
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series E. Brouwer Jaak Simm Adam Arany Yves Moreau SyDa CML AI4TS 16 289 0 29 May 2019
Adaptive Deep Kernel Learning Prudencio Tossou Basile Dura François Laviolette M. Marchand Alexandre Lacoste 21 29 0 28 May 2019
Texture Fields: Learning Texture Representations in Function Space Michael Oechsle L. Mescheder Michael Niemeyer Thilo Strauss Andreas Geiger 3DH 3DV 33 296 0 17 May 2019
Meta reinforcement learning as task inference Jan Humplik Alexandre Galashov Leonard Hasenclever Pedro A. Ortega Yee Whye Teh N. Heess OffRL 20 127 0 15 May 2019
Attentive Neural Processes Hyunjik Kim A. Mnih Jonathan Richard Schwarz M. Garnelo S. M. Ali Eslami Dan Rosenbaum Oriol Vinyals Yee Whye Teh 33 429 0 17 Jan 2019
Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data S. M. Kia A. Marquand 22 25 0 12 Dec 2018
Probabilistic Semantic Inpainting with Pixel Constrained CNNs Emilien Dupont S. Suresha 26 14 0 08 Oct 2018
HG-DAgger: Interactive Imitation Learning with Human Experts Michael Kelly Chelsea Sidrane Katherine Driggs-Campbell Mykel J. Kochenderfer OffRL 11 218 0 05 Oct 2018
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn Kelvin Xu Sergey Levine BDL 167 666 0 07 Jun 2018
Meta-Learning Probabilistic Inference For Prediction Jonathan Gordon J. Bronskill Matthias Bauer Sebastian Nowozin Richard E. Turner BDL 42 263 0 24 May 2018
Deep Learning in Mobile and Wireless Networking: A Survey Chaoyun Zhang P. Patras Hamed Haddadi 36 1,303 0 12 Mar 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 329 11,681 0 09 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 86 271 0 24 Feb 2014