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The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
GiuliaDenevi, MassimilianoPontil, CarloCiliberto....
Published date-12/01/2020
Meta-Learning
Biased regularization and fine tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks’ target vectors are all …
Disentangling Label Distribution for Long-tailed Visual Recognition
YoungkyuHong, SeungjuHan, KwangheeChoi....
Published date-12/01/2020
The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution. Such protocol …
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
KionFallah, AdamWillats, NinghaoLiu....
Published date-12/01/2020
DimensionalityReduction
Sparse coding is an important method for unsupervised learning of task-independent features in theoretical neuroscience models of neural coding. While a number of algorithms exist to learn these representations from …
Stochastic Deep Gaussian Processes over Graphs
NaiqiLi, WenjieLi, JifengSun....
Published date-12/01/2020
GaussianProcesses, VariationalInference
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains. The …
Unsupervised Learning of Object Landmarks via Self-Training Correspondence
DimitriosMallis, EnriqueSanchez, MatthewBell....
Published date-12/01/2020
Clustering
This paper addresses the problem of unsupervised discovery of object landmarks. We take a different path compared to that of existing works, based on 2 novel perspectives: (1) Self-training: starting …
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
ShreyasFadnavis, JoshuaBatson, EleftheriosGaryfallidis....
Published date-12/01/2020
Denoising, Self-SupervisedLearning
Diffusion-weighted magnetic resonance imaging (DWI) is the only non-invasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant noise in …