 Abstract—The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between
The advancements of deep neural networks (DNNs) have led to their deployment in diverse settings, including safety and security-critical applications. As a result, the characteristics of these models have become sensitive intellectual properties that require protection from malicious users. Extracting the architecture of a DNN through leaky side-channels (e.g., memory access) allows adversaries to (i) clone the model, and (ii) craft adversarial attacks. DNN obfuscation thwarts side-channel-based architectur
I just got back from ICLR 2019 and presented 2 posters, (and Michael gave a great talk!) at the Theoretical Physics Workshop on AI. To my amazement, some people still think that VC theory applies to Deep Learning, and that it is surprising that Deep Nets can overfit randomly labeled data. What's the story ? …
As the scale of artificial neural networks continues to expand to tackle increasingly complex tasks or improve the prediction accuracy of specific tasks, the challenges associated with computational demand, hyper-parameter tuning, model interpretability, and deployment costs intensify. Addressing these challenges requires a deeper understanding of how network structures influence network performance. Here, we analyse 882,000 motifs to reveal the functional roles of incoherent and coherent three-node motifs
# NRAM: Neural Random Access Memory In previous posts, we started with basic neural network architectures ( multilayer perceptrons ) and then continued with specialized architectures for image recognition and object classification ( convolutional networks ), sequence learning ( recurrent networks ), and speech recognition ( bidirectional RNNs and LSTM networks ). In the past two years, the success of deep learning has prompted more study into other problems that neural networks can solve – and the network
# No, We Need a Neural Network M.A. is one of the world's foremost experts on neural networks. His undergraduate specialty was artificial intelligence, his master's thesis was about genetic algorithms, and his doctoral dissertation covered evolutionary programming. Such an extensive computer science education opened up a wide range of career options, ranging from a professor at a university to ... a professor at another university. When someone outside of academia sought out his expertise for a project, he
This tutorial will give you a step by step guide to implementing a simple recurrent neural network in TensorFlow
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Skip to content Zenke Lab Computational Neuroscience at the FMI Selected talks from the lab Research Funding Spiking Heidelberg Digits and Spiking Speech Commands Auryn Spiking Network Simulator LaTeX rebuttal/response to reviewers template Great free text books Fluctuation-driven initialization for spiking neural network training June 23, 2022October 6, 2022 fzenke Surrogate gradients are a great tool for training spiking neural networks in computational neuroscience and neuromorphic engineering, but what