181 citations · 702 across the 49 of their papers we have counts for
6 papers · 1 filter
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Reinhard Heckel, Mahdi Soltanolkotabi
Convolutional Neural Networks (CNNs) have emerged as highly successful tools for image generation, recovery, and restoration. A major contributing factor to this success is that co…
Image recognition from raw labels collected without annotators
Fatih Furkan Yilmaz, Reinhard Heckel
Image classification problems are typically addressed by first collecting examples with candidate labels, second cleaning the candidate labels manually, and third training a deep n…
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
Zhenwei Dai, Reinhard Heckel
Normalization layers are widely used in deep neural networks to stabilize training. In this paper, we consider the training of convolutional neural networks with gradient descent o…
Regularizing linear inverse problems with convolutional neural networks
Reinhard Heckel
Deep convolutional neural networks trained on large datsets have emerged as an intriguing alternative for compressing images and solving inverse problems such as denoising and comp…
Capacity Results for the Noisy Shuffling Channel
Ilan Shomorony, Reinhard Heckel
Motivated by DNA-based storage, we study the noisy shuffling channel, which can be seen as the concatenation of a standard noisy channel (such as the BSC) and a shuffling channel,…
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel
Algorithms often carry out equally many computations for "easy" and "hard" problem instances. In particular, algorithms for finding nearest neighbors typically have the same runnin…