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07/03/2023
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search
Deep learning is increasingly impacting various aspects of contemporary ...
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06/05/2023
Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks
The remarkable proliferation of deep learning across various industries ...
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05/17/2023
Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques
Acquiring and annotating suitable datasets for training deep learning mo...
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04/10/2023
Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning
The Transformer is a highly successful deep learning model that has revo...
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02/03/2023
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits
The use of Neural Architecture Search (NAS) techniques to automate the d...
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01/28/2023
Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits
Today, artificial neural networks are the state of the art for solving a...
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01/28/2023
Heterogeneous Datasets for Federated Survival Analysis Simulation
Survival analysis studies time-modeling techniques for an event of inter...
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12/13/2022
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification
The automated machine learning (AutoML) field has become increasingly re...
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10/06/2022
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique
Automating the research for the best neural network model is a task that...
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09/07/2022
On the utility and protection of optimization with differential privacy and classic regularization techniques
Nowadays, owners and developers of deep learning models must consider st...
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09/24/2021
A Generative Federated Learning Framework for Differential Privacy
In machine learning, differential privacy and federated learning concept...
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07/28/2021
