Search Results for author:
Found 12 papers, 6 papers with code
Towards Out-of-Distribution Adversarial Robustness
Compared to existing methods, we obtain similar or superior worst-case adversarial robustness on attacks seen during training.
Learning Robust Kernel Ensembles with Kernel Average Pooling
Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations.
Adversarial Feature Desensitization
Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs.
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
We therefore developed Brain-Score – a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition – and we deployed it to evaluate a wide range of state-of-the-art deep ANNs.
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.
Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
Deep artificial neural networks with spatially repeated processing (a. k. a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream.
Continual Learning with Self-Organizing Maps
Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones.
Teacher Guided Architecture Search
We further show that measurements from only ~300 neurons from primate visual system provides enough signal to find a network with an Imagenet top-1 error that is significantly lower than that achieved by performance-guided architecture search alone.
A Neurobiological Evaluation Metric for Neural Network Model Search
In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior.
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
Mental State Recognition via Wearable EEG
The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical.
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.
