A Framework and Benchmark for Deep Batch Active Learning for Regression

17 March 2022

Papers citing "A Framework and Benchmark for Deep Batch Active Learning for Regression"

50 / 65 papers shown

Title
Efficient Data Selection for Training Genomic Perturbation Models G. Panagopoulos J. Lutzeyer Sofiane Ennadir Michalis Vazirgiannis Jun Pang 418 0 0 18 Mar 2025
Active Learning for Neural PDE Solvers Daniel Musekamp Marimuthu Kalimuthu David Holzmüller Makoto Takamoto Carlos Fernandez AI4CE 108 7 0 02 Aug 2024
Gradient Aligned Regression via Pairwise Losses Dixian Zhu Tianbao Yang Livnat Jerby-Arnon 61 0 0 08 Feb 2024
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels Mohamad Amin Mohamadi Wonho Bae Danica J. Sutherland 58 21 0 25 Jun 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 49 54 0 17 Jun 2022
Simulation Intelligence: Towards a New Generation of Scientific Methods Alexander Lavin D. Krakauer Hector Zenil Justin Emile Gottschlich Tim Mattson ... A. Hanuka Manuela Veloso Samuel A. Assefa Stephan Zheng Avi Pfeffer 116 110 0 06 Dec 2021
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning Runa Eschenhagen Erik A. Daxberger Philipp Hennig Agustinus Kristiadi UQCV BDL 52 22 0 05 Nov 2021
Neural Networks as Kernel Learners: The Silent Alignment Effect Alexander B. Atanasov Blake Bordelon Cengiz Pehlevan MLT 62 83 0 29 Oct 2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery Arash Mehrjou Ashkan Soleymani Andrew Jesson Pascal Notin Y. Gal Stefan Bauer Patrick Schwab 66 21 0 22 Oct 2021
Deep Active Learning by Leveraging Training Dynamics Haonan Wang Wei Huang Ziwei Wu A. Margenot Hanghang Tong Jingrui He AI4CE 45 34 0 16 Oct 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 205 312 0 28 Jun 2021
Revisiting Deep Learning Models for Tabular Data Yu. V. Gorishniy Ivan Rubachev Valentin Khrulkov Artem Babenko LMTD 100 748 0 22 Jun 2021
Well-tuned Simple Nets Excel on Tabular Datasets Arlind Kadra Marius Lindauer Frank Hutter Josif Grabocka 38 195 0 21 Jun 2021
Gone Fishing: Neural Active Learning with Fisher Embeddings Jordan T. Ash Surbhi Goel A. Krishnamurthy Sham Kakade 56 88 0 17 Jun 2021
Scaling Neural Tangent Kernels via Sketching and Random Features A. Zandieh Insu Han H. Avron N. Shoham Chaewon Kim Jinwoo Shin 46 32 0 15 Jun 2021
Neural Active Learning with Performance Guarantees Pranjal Awasthi Christoph Dann Claudio Gentile Ayush Sekhari Zhilei Wang 43 22 0 06 Jun 2021
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training Gowthami Somepalli Micah Goldblum Avi Schwarzschild C. Bayan Bruss Tom Goldstein LMTD 85 325 0 02 Jun 2021
A Theory of Neural Tangent Kernel Alignment and Its Influence on Training H. Shan Blake Bordelon 40 11 0 29 May 2021
Properties of the After Kernel Philip M. Long 41 29 0 21 May 2021
Data Shapley Valuation for Efficient Batch Active Learning Amirata Ghorbani James Zou A. Esteva TDI 29 37 0 16 Apr 2021
Random Features for the Neural Tangent Kernel Insu Han H. Avron N. Shoham Chaewon Kim Jinwoo Shin 48 9 0 03 Apr 2021
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks Apoorva Sharma Navid Azizan Marco Pavone UQCV 69 46 0 24 Feb 2021
On Statistical Bias In Active Learning: How and When To Fix It Sebastian Farquhar Y. Gal Tom Rainforth TDI HAI 40 85 0 27 Jan 2021
Which Minimizer Does My Neural Network Converge To? Manuel Nonnenmacher David Reeb Ingo Steinwart ODL 32 4 0 04 Nov 2020
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort Gintare Karolina Dziugaite Mansheej Paul Sepideh Kharaghani Daniel M. Roy Surya Ganguli 97 190 0 28 Oct 2020
Semi-supervised Batch Active Learning via Bilevel Optimization Zalan Borsos Marco Tagliasacchi Andreas Krause 122 23 0 19 Oct 2020
Identifying Wrongly Predicted Samples: A Method for Active Learning Rahaf Aljundi N. Chumerin Daniel Olmeda Reino 14 7 0 14 Oct 2020
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning N. Shoham H. Avron BDL 19 12 0 27 Sep 2020
A Survey of Deep Active Learning Pengzhen Ren Yun Xiao Xiaojun Chang Po-Yao (Bernie) Huang Zhihui Li Brij B. Gupta Xiaojiang Chen Xin Wang 93 1,136 0 30 Aug 2020
Improving predictions of Bayesian neural nets via local linearization Alexander Immer M. Korzepa Matthias Bauer BDL 45 11 0 19 Aug 2020
Coresets via Bilevel Optimization for Continual Learning and Streaming Zalan Borsos Mojmír Mutný Andreas Krause CLL 73 233 0 06 Jun 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 77 286 0 24 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 95 652 0 20 Feb 2020
Benchmarking the Neural Linear Model for Regression Sebastian W. Ober C. Rasmussen BDL 67 42 0 18 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 439 42,393 0 03 Dec 2019
Bayesian Batch Active Learning as Sparse Subset Approximation Robert Pinsler Jonathan Gordon Eric T. Nalisnick José Miguel Hernández-Lobato UQCV 47 132 0 06 Aug 2019
Selection via Proxy: Efficient Data Selection for Deep Learning Cody Coleman Christopher Yeh Stephen Mussmann Baharan Mirzasoleiman Peter Bailis Percy Liang J. Leskovec Matei A. Zaharia 73 346 0 26 Jun 2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch Joost R. van Amersfoort Y. Gal FedML 85 627 0 19 Jun 2019
Batch Active Learning Using Determinantal Point Processes Erdem Biyik Kenneth Wang Nima Anari Dorsa Sadigh 57 62 0 19 Jun 2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds Jordan T. Ash Chicheng Zhang A. Krishnamurthy John Langford Alekh Agarwal BDL UQCV 85 772 0 09 Jun 2019
Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan Alexander Immer Ehsan Abedi M. Korzepa UQCV BDL 83 125 0 05 Jun 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 213 922 0 26 Apr 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 205 1,099 0 18 Feb 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 807 0 07 Feb 2019
Diverse mini-batch Active Learning Fedor Zhdanov 55 155 0 17 Jan 2019
Explaining Aggregates for Exploratory Analytics Fotis Savva Christos Anagnostopoulos Peter Triantafillou 28 18 0 29 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 102 833 0 19 Dec 2018
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles Remus Pop Patric Fulop UQCV 53 41 0 09 Nov 2018
Dropout-based Active Learning for Regression Evgenii Tsymbalov Maxim Panov Alexander Shapeev BDL UQCV 28 56 0 26 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 261 3,194 0 20 Jun 2018