200 citations · 311 across the 21 of their papers we have counts for
22 papers
Fully Variational Noise-Contrastive Estimation
Christopher Zach
By using the underlying theory of proper scoring rules, we design a family of noise-contrastive estimation (NCE) methods that are tractable for latent variable models. Both terms i…
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
Rasmus Høier, D. Staudt, Christopher Zach
Activity difference based learning algorithms-such as contrastive Hebbian learning and equilibrium propagation-have been proposed as biologically plausible alternatives to error ba…
Extremely Low-light Image Enhancement with Scene Text Restoration
Pohao Hsu, Che-Tsung Lin, Chun Chet Ng +5
Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, w…
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Huu Le, Rasmus Kjær Høier, Che-Tsung Lin +1
We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bi…
BabelCalib: A Universal Approach to Calibrating Central Cameras
Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen +3
Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parame…
Bilevel Programs Meet Deep Learning: A Unifying View on Inference Learning Methods
Christopher Zach
In this work we unify a number of inference learning methods, that are proposed in the literature as alternative training algorithms to the ones based on regular error back-propaga…