27 citations · 111 across the 28 of their papers we have counts for
28 papers
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi, Lars Kotthoff, Peijie Chen +1
Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria…
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Julia Moosbauer, Martin Binder, Lennart Schneider +5
Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks. The process of designing HPO a…
Bayesian Optimization in Materials Science: A Survey
Lars Kotthoff, Hud Wahab, Patrick Johnson
Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applicat…
Modeling and Optimizing Laser-Induced Graphene
Lars Kotthoff, Sourin Dey, Vivek Jain +3
A lot of technological advances depend on next-generation materials, such as graphene, which enables a raft of new applications, for example better electronics. Manufacturing such…
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks
Md Shahriar Iqbal, Jianhai Su, Lars Kotthoff +1
The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typica…
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Md Shahriar Iqbal, Lars Kotthoff, Pooyan Jamshidi
Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time…