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Applications of Deep Neural Networks with Keras
Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output.
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.
Generative Moment Matching Networks
We consider the problem of learning deep generative models from data.
Contrastive Code Representation Learning
Recent work learns contextual representations of source code by reconstructing tokens from their context.
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Symbolic Execution for Deep Neural Networks
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns.
Software Engineering Cryptography and Security
A Variational Inequality Perspective on Generative Adversarial Networks
Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
The resulting SDP can be adapted to increase either the estimation accuracy (by capturing the interaction between activation functions of different layers) or scalability (by decomposition and parallel implementation).
Broken Neural Scaling Laws
Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic.
Ptolemy: Architecture Support for Robust Deep Learning
We propose Ptolemy, an algorithm-architecture co-designed system that detects adversarial attacks at inference time with low overhead and high accuracy. We exploit the synergies between DNN inference and imperative program execution: an input to a DNN uniquely activates a set of neurons that contribute significantly to the inference output, analogous to the sequence of basic blocks exercised by an input in a conventional program.
Hardware Architecture Signal Processing
Sparse Adversarial Attack via Perturbation Factorization
Based on this factorization, we formulate the sparse attack problem as a mixed integer programming (MIP) to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
