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Registration of serial sections: An evaluation method based on distortions of the ground truths
OlegLobachev, TakuyaFunatomi, AlexanderPfaffenroth....
Published date-11/22/2020
MedicalImageRegistration
Registration of histological serial sections is a challenging task. Serial sections exhibit distortions from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D …
Learning a Deep Generative Model like a Program: the Free Category Prior
EliSennesh....
Published date-11/22/2020
Programinduction
Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we …
A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
MarcelloSerqueira, PedroGonzález, EduardoBezerra....
Published date-11/22/2020
HyperparameterOptimization
In recent years, large amounts of data have been generated, and computer power has kept growing. This scenario has led to a resurgence in the interest in artificial neural networks. …
A Homotopy-based Algorithm for Sparse Multiple Right-hand Sides Nonnegative Least Squares
NicolasNadisic, ArnaudVandaele, NicolasGillis....
Published date-11/22/2020
Nonnegative least squares (NNLS) problems arise in models that rely on additive linear combinations. In particular, they are at the core of nonnegative matrix factorization (NMF) algorithms. The nonnegativity constraint …
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
BrettD.Roads, BradleyC.Love....
Published date-11/22/2020
BayesianInference, ObjectRecognition
Advances in object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks---such as ILSVRC---are relatively task-specific, focusing predominately on predicting class labels. …
Robust Gaussian Process Regression Based on Iterative Trimming
Zhao-ZhouLi, LuLi, ZhengyiShao....
Published date-11/22/2020
The model prediction of the Gaussian process (GP) regression can be significantly biased when the data are contaminated by outliers. We propose a new robust GP regression algorithm that iteratively …