Search Results for author:
Found 10 papers, 3 papers with code
Self-Supervised Learning for Covariance Estimation
We consider the use of deep learning for covariance estimation.
Probabilistic Simplex Component Analysis by Importance Sampling
In this paper we consider the problem of linear unmixing hidden random variables defined over the simplex with additive Gaussian noise, also known as probabilistic simplex component analysis (PRISM).
CFARnet: deep learning for target detection with constant false alarm rate
We consider the problem of target detection with a constant false alarm rate (CFAR).
Learning to Detect with Constant False Alarm Rate
We consider the use of machine learning for hypothesis testing with an emphasis on target detection.
Learning to Estimate Without Bias
In such applications, we show that BCE leads to asymptotically consistent estimators.
cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery
Detection and classification of objects in overhead images are two important and challenging problems in computer vision.
Conditional Frechet Inception Distance
We consider distance functions between conditional distributions.
COFGA: Classification Of Fine-Grained Features In Aerial Images
Classification between thousands of classes in high-resolution images is one of the heavily studied problems in deep learning over the last decade.
Learning to Detect
In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks.
Deep MIMO Detection
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection.
