Is margin all you need? An extensive empirical study of active learning on tabular data

7 October 2022

Papers citing "Is margin all you need? An extensive empirical study of active learning on tabular data"

8 / 8 papers shown

Title
A Cross-Domain Benchmark for Active Learning M. M. Kholoosi M. A. Babar Maximilian Stubbemann Lars Schmidt-Thieme 35 1 0 01 Aug 2024
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data Valentin Margraf Marcel Wever Sandra Gilhuber Gabriel Marques Tavares Thomas Seidl Eyke Hüllermeier 41 0 0 25 Jun 2024
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification Denis Huseljic Paul Hahn M. Herde Lukas Rauch Bernhard Sick 30 1 0 13 Apr 2024
Benchmarking Multi-Domain Active Learning on Image Classification Jiayi Li Rohan Taori Tatsunori Hashimoto VLM 32 0 0 01 Dec 2023
Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning Elie Bursztein Luca Invernizzi Karel Král D. Moghimi J. Picod Marina Zhang AAML 30 5 0 12 Jun 2023
Combining Self-labeling with Selective Sampling Jedrzej Kozal Michal Wo'zniak 26 3 0 11 Jan 2023
Efficient Bayesian Updates for Deep Learning via Laplace Approximations Denis Huseljic M. Herde Lukas Rauch Paul Hahn Zhixin Huang D. Kottke S. Vogt Bernhard Sick BDL 16 0 0 12 Oct 2022
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets Guy Hacohen Avihu Dekel D. Weinshall 124 116 0 06 Feb 2022