75 citations · 169 across the 16 of their papers we have counts for
6 papers · 1 filter
Efficient Search for Customized Activation Functions with Gradient Descent
Lukas Strack, Mahmoud Safari, Frank Hutter
Different activation functions work best for different deep learning models. To exploit this, we leverage recent advancements in gradient-based search techniques for neural archite…
LMEMs for post-hoc analysis of HPO Benchmarking
Anton Geburek, Neeratyoy Mallik, Danny Stoll +2
The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in ne…
FairPFN: Transformers Can do Counterfactual Fairness
Jake Robertson, Noah Hollmann, Noor Awad +1
Machine Learning systems are increasingly prevalent across healthcare, law enforcement, and finance but often operate on historical data, which may carry biases against certain dem…
Fast Optimizer Benchmark
Simon Blauth, Tobias Bürger, Zacharias Häringer +2
In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from mu…
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier +6
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research o…
Is Mamba Capable of In-Context Learning?
Riccardo Grazzi, Julien Siems, Simon Schrodi +2
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks du…