
Personalized Federated Learning using Hypernetworks
Personalized federated learning is tasked with training machine learning...
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GPTree: A Gaussian Process Classifier for FewShot Incremental Learning
Gaussian processes (GPs) are nonparametric, flexible, models that work ...
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PositionAgnostic MultiMicrophone Speech Dereverberation
Neural networks (NNs) have been widely applied in speech processing task...
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On Size Generalization in Graph Neural Networks
Graph neural networks (GNNs) can process graphs of different sizes but t...
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Learning the Pareto Front with Hypernetworks
Multiobjective optimization problems are prevalent in machine learning....
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Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks
The main source of information regarding ancient Mesopotamian history an...
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On Learning Sets of Symmetric Elements
Learning from unordered sets is a fundamental learning setup, which is a...
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Conditional Generative Models are not Robust
Classconditional generative models are an increasingly popular approach...
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Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Predicting not only the target but also an accurate measure of uncertain...
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On the Universality of Invariant Networks
Constraining linear layers in neural networks to respect symmetry transf...
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Incremental FewShot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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Inference in Probabilistic Graphical Models by Graph Neural Networks
A useful computation when acting in a complex environment is to infer th...
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Reviving and Improving Recurrent BackPropagation
In this paper, we revisit the recurrent backpropagation (RBP) algorithm...
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Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in p...
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Learning Discrete Weights Using the Local Reparameterization Trick
Recent breakthroughs in computer vision make use of large deep neural ne...
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Human Pose Estimation using Deep Consensus Voting
In this paper we consider the problem of human pose estimation from a si...
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Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple...
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Learning Local Invariant Mahalanobis Distances
For many tasks and data types, there are natural transformations to whic...
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Graph Approximation and Clustering on a Budget
We consider the problem of learning from a similarity matrix (such as sp...
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Ethan Fetaya
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