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Found 8 papers, 5 papers with code
The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision
We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort.
MotherNet: A Foundational Hypernetwork for Tabular Classification
In contrast to existing hypernetworks that were either task-specific or trained for relatively constraint multi-task settings, MotherNet is trained to generate networks to perform multiclass classification on arbitrary tabular datasets without any dataset specific gradient descent.
On the Detection of Image-Scaling Attacks in Machine Learning
Unfortunately, this preprocessing step is vulnerable to so-called image-scaling attacks where an attacker makes unnoticeable changes to an image so that it becomes a new image after scaling.
Detecting Spells in Fantasy Literature with a Transformer Based Artificial Intelligence
For our studies a pre-trained BERT model was used and fine-tuned utilising different datasets and training methods to identify the searched context.
Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets
In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation.
OpenML-Python: an extensible Python API for OpenML
It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.
Learning Multiple Defaults for Machine Learning Algorithms
The performance of modern machine learning methods highly depends on their hyperparameter configurations.
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
