Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Diskin, Tzvi"'
Autor:
Diskin, Tzvi, Wiesel, Ami
We consider the use of deep learning for covariance estimation. We propose to globally learn a neural network that will then be applied locally at inference time. Leveraging recent advancements in self-supervised foundational models, we train the net
Externí odkaz:
http://arxiv.org/abs/2403.08662
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). Previous solutions to tackle this challenging prob
Externí odkaz:
http://arxiv.org/abs/2302.11230
We consider the problem of target detection with a constant false alarm rate (CFAR). This constraint is crucial in many practical applications and is a standard requirement in classical composite hypothesis testing. In settings where classical approa
Externí odkaz:
http://arxiv.org/abs/2208.02474
We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally expensive. In co
Externí odkaz:
http://arxiv.org/abs/2206.05747
The Gauss Markov theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a first step towards extending this result to non-linear settings via deep lear
Externí odkaz:
http://arxiv.org/abs/2110.12403
Publikováno v:
In Signal Processing October 2024 223
Detection and classification of objects in overhead images are two important and challenging problems in computer vision. Among various research areas in this domain, the task of fine-grained classification of objects in overhead images has become ub
Externí odkaz:
http://arxiv.org/abs/2105.12786
We consider distance functions between conditional distributions. We focus on the Wasserstein metric and its Gaussian case known as the Frechet Inception Distance (FID). We develop conditional versions of these metrics, analyze their relations and pr
Externí odkaz:
http://arxiv.org/abs/2103.11521
Autor:
Dahan, Eran, Diskin, Tzvi
Classification between thousands of classes in high-resolution images is one of the heavily studied problems in deep learning over the last decade. However, the challenge of fine-grained multi-class classification of objects in aerial images, especia
Externí odkaz:
http://arxiv.org/abs/1808.09001
In this paper we consider Multiple-Input-Multiple-Output (MIMO) detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a Detection Network (DetNet) which is specificall
Externí odkaz:
http://arxiv.org/abs/1805.07631