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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 …
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. …
Predictive process mining by network of classifiers and clusterers: the PEDF model
AmirMohammadEsmaieeliSikaroudi, MdHabiborRahman....
Published date-11/22/2020
In this research, a model is proposed to learn from event log and predict future events of a system. The proposed PEDF model learns based on events' sequences, durations, and …
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 …
Multiresolution Knowledge Distillation for Anomaly Detection
MohammadrezaSalehi, NioushaSadjadi, SorooshBaselizadeh....
Published date-11/22/2020
AnomalyDetection, RepresentationLearning, UnsupervisedRepresentationLearning
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not …
Differentiable Computational Geometry for 2D and 3D machine learning
YuanxinZhong....
Published date-11/22/2020
With the growth of machine learning algorithms with geometry primitives, a high-efficiency library with differentiable geometric operators are desired. We present an optimized Differentiable Geometry Algorithm Library (DGAL) loaded with …