
Rapid Model Architecture Adaption for MetaLearning
Network Architecture Search (NAS) methods have recently gathered much at...
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Initialization and Regularization of Factorized Neural Layers
Factorized layers–operations parameterized by products of two or more ma...
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Gradient Flows in Dataset Space
The current practice in machine learning is traditionally modelcentric,...
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Modelspecific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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Weighted MetaLearning
Metalearning leverages related source tasks to learn an initialization ...
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Geometric Dataset Distances via Optimal Transport
The notion of task similarity is at the core of various machine learning...
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Feature Gradients: Scalable Feature Selection via Discrete Relaxation
In this paper we introduce Feature Gradients, a gradientbased search al...
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Probabilistic Neural Architecture Search
In neural architecture search (NAS), the space of neural network archite...
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Model Compression with Generative Adversarial Networks
More accurate machine learning models often demand more computation and ...
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Gaussian Process Prior Variational Autoencoders
Variational autoencoders (VAE) are a powerful and widelyused class of m...
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Probabilistic Matrix Factorization for Automated Machine Learning
In order to achieve stateoftheart performance, modern machine learnin...
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Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process model...
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Nicolo Fusi
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