67 citations · 164 across the 16 of their papers we have counts for
13 papers · 1 filter
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network
Haozhe Sun, Isabelle Guyon, Felix Mohr +1
It has become mainstream in computer vision and other machine learning domains to reuse backbone networks pre-trained on large datasets as preprocessors. Typically, the last layer…
Is One Epoch All You Need For Multi-Fidelity Hyperparameter Optimization?
Romain Egele, Isabelle Guyon, Yixuan Sun +1
Hyperparameter optimization (HPO) is crucial for fine-tuning machine learning models but can be computationally expensive. To reduce costs, Multi-fidelity HPO (MF-HPO) leverages in…
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Zhen Xu, Lanning Wei, Huan Zhao +4
Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
LTU Attacker for Membership Inference
Joseph Pedersen, Rafael Muñoz-Gómez, Jiangnan Huang +3
We address the problem of defending predictive models, such as machine learning classifiers (Defender models), against membership inference attacks, in both the black-box and white…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…