Deep Neural Networks

IBM Journal of Research & Development Spotlights AI Hardware

The fourth-quarter issue of the IBM Journal of Research & Development is dedicated to the exploration and deployment of hardware for AI systems. It contains 10 contributions from leading authorities in the fields that summarize the latest state of the art and share new research results.

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Adversarial Learning and Zeroth Order Optimization for Machine Learning and Data Mining

There is a growing number of adversarial attacks and nefarious behaviors aimed at AI systems. To combat this, IBM Research AI will present multiple papers that yield new scientific discoveries and recommendations related to adversarial learning at KDD 2019.

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TAPAS: Frugally Predicting the Accuracy of a Neural Network Prior to Training

Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. What if you could forecast the accuracy of the neural network earlier thanks to accumulated experience and approximation? This was the goal of a recent project at IBM Research and the result is TAPAS or Train-less Accuracy Predictor […]

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The Adversarial Robustness Toolbox: Securing AI Against Adversarial Threats

Recent years have seen tremendous advances in the development of artificial intelligence (AI). Modern AI systems achieve human-level performance on cognitive tasks such as recognizing objects in images, annotating videos, converting speech to text, or translating between different languages. Many of these breakthrough results are based on Deep Neural Networks (DNNs). DNNs are complex machine […]

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