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Found 9 papers, 5 papers with code
A Robust Learning Approach to Domain Adaptive Object Detection
To adapt to the domain shift, the model is trained on the target domain using a set of noisy object bounding boxes that are obtained by a detection model trained only in the source domain.
Undirected Graphical Models as Approximate Posteriors
We extend the class of posterior models that may be learned by using undirected graphical models.
Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming.
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
Experiments on the MNIST and OMNIGLOT datasets show that these relaxations outperform previous discrete VAEs with Boltzmann priors.
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult.
Ranked #53 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.
A practical heuristic for finding graph minors
We present a heuristic algorithm for finding a graph $H$ as a minor of a graph $G$ that is practical for sparse $G$ and $H$ with hundreds of vertices.
Quantum Physics Data Structures and Algorithms Combinatorics 05C83, 81P68
A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration
We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights.
Quantum Physics
Training a Binary Classifier with the Quantum Adiabatic Algorithm
To bring it into a format that allows the application of adiabatic quantum computing (AQC), we first show that the bit-precision with which the weights need to be represented only grows logarithmically with the ratio of the number of training examples to the number of weak classifiers.
Quantum Physics
