Search Results for author:
Found 9 papers, 6 papers with code
Clifford-Steerable Convolutional Neural Networks
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p, q)$-equivariant CNNs.
Unveiling Empirical Pathologies of Laplace Approximation for Uncertainty Estimation
In this paper, we critically evaluate Bayesian methods for uncertainty estimation in deep learning, focusing on the widely applied Laplace approximation and its variants.
Catching Image Retrieval Generalization
The concepts of overfitting and generalization are vital for evaluating machine learning models.
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection.
Machine learning-assisted close-set X-ray diffraction phase identification of transition metals
Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results.
Implicit Convolutional Kernels for Steerable CNNs
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations.
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials.
Learning Generative Factors of EEG Data with Variational auto-encoders
Electroencephalography produces high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest.
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
One such pathology is schizophrenia which is often followed by auditory verbal hallucinations.
