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Mute Background Noise | Noise Cancelling Software | krisp
Take calls from wherever you want without being embarassed
for a background noise. Get krisp for Mac and use with any conferecing app!
sound  background  suppression  noise  dnn 
28 days ago by gnuf
[1811.03804] Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. Our bounds also shed light on the advantage of using ResNet over the fully connected feedforward architecture; our bound requires the number of neurons per layer scaling exponentially with depth for feedforward networks whereas for ResNet the bound only requires the number of neurons per layer scaling polynomially with depth. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result.
dnn  neural-net  analysis  gradient-descent  optimization 
4 weeks ago by arsyed
vdumoulin/conv_arithmetic: A technical report on convolution arithmetic in the context of deep learning
A technical report on convolution arithmetic in the context of deep learning - vdumoulin/conv_arithmetic
visual  visualisation  neural  network  cnn  dnn  convolution  ai  learning 
7 weeks ago by severin.smith
High Performance Computing for Big Data: Methodologies and Applications - Google Livros
High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, a...
dnn  energy 
11 weeks ago by carlosviansi
Verharmlosung gehört hoffentlich auch bei den nicht zum journalistischen Handwerk.…
DNN  from twitter_favs
august 2018 by reinhard_codes
[1807.03819] Universal Transformers
Self-attentive feed-forward sequence models have been shown to achieve impressive results on sequence modeling tasks, thereby presenting a compelling alternative to recurrent neural networks (RNNs) which has remained the de-facto standard architecture for many sequence modeling problems to date. Despite these successes, however, feed-forward sequence models like the Transformer fail to generalize in many tasks that recurrent models handle with ease (e.g. copying when the string lengths exceed those observed at training time). Moreover, and in contrast to RNNs, the Transformer model is not computationally universal, limiting its theoretical expressivity. In this paper we propose the Universal Transformer which addresses these practical and theoretical shortcomings and we show that it leads to improved performance on several tasks. Instead of recurring over the individual symbols of sequences like RNNs, the Universal Transformer repeatedly revises its representations of all symbols in the sequence with each recurrent step. In order to combine information from different parts of a sequence, it employs a self-attention mechanism in every recurrent step. Assuming sufficient memory, its recurrence makes the Universal Transformer computationally universal. We further employ an adaptive computation time (ACT) mechanism to allow the model to dynamically adjust the number of times the representation of each position in a sequence is revised. Beyond saving computation, we show that ACT can improve the accuracy of the model. Our experiments show that on various algorithmic tasks and a diverse set of large-scale language understanding tasks the Universal Transformer generalizes significantly better and outperforms both a vanilla Transformer and an LSTM in machine translation, and achieves a new state of the art on the bAbI linguistic reasoning task and the challenging LAMBADA language modeling task.
july 2018 by foodbaby

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