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03/19/2022
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection
Unsupervised Anomaly detection (AD) requires building a notion of normal...
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06/18/2021
Evolving GANs: When Contradictions Turn into Compliance
Limited availability of labeled-data makes any supervised learning probl...
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07/27/2020
Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization
This research proposes to use the Moreau-Yosida envelope to stabilize th...
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05/08/2020
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey
With the general trend of increasing Convolutional Neural Network (CNN) ...
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11/06/2019
Auptimizer – an Extensible, Open-Source Framework for Hyperparameter Tuning
Tuning machine learning models at scale, especially finding the right hy...
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11/02/2019
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
The current paradigm for using machine learning models on a device is to...
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05/14/2019
Robust Neural Network Training using Periodic Sampling over Model Weights
Deep neural networks provide best-in-class performance for a number of c...
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11/27/2018
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
With recent advances in learning algorithms and hardware development, au...
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07/02/2018
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
Deep Neural Networks (DNNs) have become increasingly popular in computer...
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01/25/2018
