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04/28/2023
Hyperparameter Optimization through Neural Network Partitioning
Well-tuned hyperparameters are crucial for obtaining good generalization...
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06/22/2022
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Federated Learning (FL) is a machine learning paradigm to distributively...
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11/19/2021
An Expectation-Maximization Perspective on Federated Learning
Federated learning describes the distributed training of models across m...
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11/09/2021
DP-REC: Private Communication-Efficient Federated Learning
Privacy and communication efficiency are important challenges in federat...
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07/14/2021
Federated Mixture of Experts
Federated learning (FL) has emerged as the predominant approach for coll...
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04/18/2021
Federated Learning of User Verification Models Without Sharing Embeddings
We consider the problem of training User Verification (UV) models in fed...
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08/25/2020
Improving Fair Predictions Using Variational Inference In Causal Models
The importance of algorithmic fairness grows with the increasing impact ...
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07/09/2020
Federated Learning of User Authentication Models
Machine learning-based User Authentication (UA) models have been widely ...
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05/14/2020
Bayesian Bits: Unifying Quantization and Pruning
We introduce Bayesian Bits, a practical method for joint mixed precision...
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04/22/2020
Up or Down? Adaptive Rounding for Post-Training Quantization
When quantizing neural networks, assigning each floating-point weight to...
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02/18/2020
Gradient ℓ_1 Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural ne...
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06/19/2019
The Functional Neural Process
We present a new family of exchangeable stochastic processes, the Functi...
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05/24/2019
DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
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10/03/2018
Relaxed Quantization for Discretized Neural Networks
Neural network quantization has become an important research area due to...
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12/04/2017
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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05/24/2017
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such a...
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05/24/2017
Bayesian Compression for Deep Learning
Compression and computational efficiency in deep learning have become a ...
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03/06/2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
We reinterpret multiplicative noise in neural networks as auxiliary rand...
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03/15/2016
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
We introduce a variational Bayesian neural network where the parameters ...
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11/03/2015
