
Federated Learning of User Authentication Models
Machine learningbased User Authentication (UA) models have been widely ...
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Bayesian Bits: Unifying Quantization and Pruning
We introduce Bayesian Bits, a practical method for joint mixed precision...
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Up or Down? Adaptive Rounding for PostTraining Quantization
When quantizing neural networks, assigning each floatingpoint weight to...
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Gradient ℓ_1 Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural ne...
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The Functional Neural Process
We present a new family of exchangeable stochastic processes, the Functi...
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DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
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Relaxed Quantization for Discretized Neural Networks
Neural network quantization has become an important research area due to...
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Learning Sparse Neural Networks through L_0 Regularization
We propose a practical method for L_0 norm regularization for neural net...
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Causal Effect Inference with Deep LatentVariable Models
Learning individuallevel causal effects from observational data, such a...
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Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a ...
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Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
We reinterpret multiplicative noise in neural networks as auxiliary rand...
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Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
We introduce a variational Bayesian neural network where the parameters ...
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The Variational Fair Autoencoder
We investigate the problem of learning representations that are invarian...
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Christos Louizos
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