research
∙
04/24/2023
Renate: A Library for Real-World Continual Learning
Continual learning enables the incremental training of machine learning ...
share
research
∙
02/21/2023
Variational Boosted Soft Trees
Gradient boosting machines (GBMs) based on decision trees consistently d...
research
∙
02/01/2023
Deep Power Laws for Hyperparameter Optimization
Hyperparameter optimization is an important subfield of machine learning...
research
∙
06/28/2022
Continual Learning with Transformers for Image Classification
In many real-world scenarios, data to train machine learning models beco...
research
∙
03/09/2022
Memory Efficient Continual Learning for Neural Text Classification
Learning text classifiers based on pre-trained language models has becom...
research
∙
02/20/2022
Dynamic and Efficient Gray-Box Hyperparameter Optimization for Deep Learning
Gray-box hyperparameter optimization techniques have recently emerged as...
research
∙
06/11/2021
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Hyperparameter optimization (HPO) is a core problem for the machine lear...
research
∙
01/22/2021
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Neural Architecture Search (NAS) methods have been growing in popularity...
research
∙
01/19/2021
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
Hyperparameter optimization (HPO) is a central pillar in the automation ...
research
∙
06/05/2020
Learning to Rank Learning Curves
Many automated machine learning methods, such as those for hyperparamete...
research
∙
10/22/2019
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily...
research
∙
07/18/2019
XferNAS: Transfer Neural Architecture Search
The term Neural Architecture Search (NAS) refers to the automatic optimi...
research
∙
05/04/2019
A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep...
research
∙
03/08/2019
Inductive Transfer for Neural Architecture Optimization
The recent advent of automated neural network architecture search led to...
research
∙
01/17/2019
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applicatio...
research
∙
07/03/2018
Adversarial Robustness Toolbox v0.2.2
Adversarial examples have become an indisputable threat to the security ...
research
∙
06/15/2018
Automated Image Data Preprocessing with Deep Reinforcement Learning
Data preparation, i.e. the process of transforming raw data into a forma...
research
∙
06/07/2018
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data
We introduce a new Bayesian multi-class support vector machine by formul...
research
∙
01/16/2018
Learning Features For Relational Data
Feature engineering is one of the most important but tedious tasks in da...
research
∙
12/20/2017
Finding Competitive Network Architectures Within a Day Using UCT
The design of neural network architectures for a new data set is a labor...
research
∙
11/22/2017
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Deep Learning models are vulnerable to adversarial examples, i.e. images...
research
∙
10/13/2016
Bank Card Usage Prediction Exploiting Geolocation Information
We describe the solution of team ISMLL for the ECML-PKDD 2016 Discovery ...
research
∙
07/24/2013