Expansion property of a graph refers to its strong connectivity as well as sparseness. It has been reported that deep neural networks can be pruned to a high degree of sparsity while maintaining their performance. Such pruning is essential for performing real time sequence learning tasks using recurrent neural networks in resource constrained platforms. We prune recurrent networks such as RNNs and LSTMs, maintaining a large spectral gap of the underlying graphs and ensuring their layerwise expansion properties. We also study the time unfolded recurrent network graphs in terms of the properties of their bipartite layers. Experimental results for the benchmark sequence MNIST, CIFAR-10, and Google speech command data show that expander graph properties are key to preserving classification accuracy of RNN and LSTM. Analysis of Artificial Neural Networks (ANNs) following a connection base approach is a topical research direction as this not only mimics brain networks in neuroscience, but also can provide specific graph measures which can be used for analysis of performance and robustness of the networks. Researchers in the recent years have explored if there is a relation between the functional aspects of an ANN and its graph structure, and if such a relation does exist then are there any characterization that explains the relationship between the structure of the graph and is performance LeCun et al. (1998); Sermanet et al. (2013); Zeiler & Fergus (2014); Krizhevsky et al. (2017); Simonyan & Zisserman (2014); He et al. (2016); Szegedy et al. (2015).

In the realm of 5G communication systems, the accuracy of Channel State Information (CSI) prediction is vital for optimizing performance. This letter introduces a pioneering approach: the Spectral-Temporal Graph Neural Network (STEM GNN), which fuses spatial relationships and temporal dynamics of the wireless channel using the Graph Fourier Transform. We compare the STEM GNN approach with conventional Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for CSI prediction. Our findings reveal a significant enhancement in overall communication system performance through STEM GNNs. For instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns often overlooked by traditional models, offering improvements in beamforming, interference mitigation, and ultra-reliable low-latency communication (URLLC).

Black Sigatoka disease severely decreases global banana production, and climate change aggravates the problem by altering fungal species distributions. Due to the heavy financial burden of managing this infectious disease, farmers in developing countries face significant banana crop losses. Though scientists have produced mathematical models of infectious diseases, adapting these models to incorporate climate effects is difficult. We present MR. NODE (Multiple predictoR Neural ODE), a neural network that models the dynamics of black Sigatoka infection learnt directly from data via Neural Ordinary Differential Equations. Our method encodes external predictor factors into the latent space in addition to the variable that we infer, and it can also predict the infection risk at an arbitrary point in time. Empirically, we demonstrate on historical climate data that our method has superior generalization performance on time points up to one month in the future and unseen irregularities. We believe that our method can be a useful tool to control the spread of black Sigatoka.

After going through a lot of theoretical articles on recurrent layers, I just wanted to build my first LSTM model and train it on some texts! But the huge list of exposed parameters for the layer and the delicacies of layer structures were too complicated for me. This meant I had to spend a lot of time going through StackOverflow and API definitions to get a clearer picture. This article is an attempt to consolidate all of the notes which can accelerate the process of transition from theory to practice. The goal of this guide is to develop a practical understanding of using recurrent layers like RNN and LSTM rather than to provide theoretical understanding.

Now in its 37th year, ICML (The International Conference on Machine Learning) is known for bringing cutting-edge research on all aspects of machine learning to the fore. This year, 1088 papers have been accepted from 4990 submissions. Here are a few interesting works to look at ICML 2020, which will be held between 13th and 18th of July. Meta-learning relies on deep networks, which makes batch normalization an essential component of meta-learning pipelines. However, there are several challenges that can render conventional batch normalization ineffective, giving rise to the need to rethink normalization in this setting.

As you read this article, you understand each word based on your understanding of previous words. You don't throw everything away and start thinking from scratch again. We have already seen in Introduction to Artificial Neural Networks(ANN) how ANN can be used for regression and classification tasks, and in Introduction to Convolutional Neural Networks(CNN) how CNN can be used for image recognition, segmentation or object detection and computer-vision related tasks. But what if we have sequential data? Before we dig into details of Recurrent Neural networks, if you are a beginner I suggest you read below two articles to get a basic understanding of neural networks.

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.

Many of the most impressive advances in natural language processing and AI chatbots are driven by Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are special neural network architectures that are able to process sequential data, data where chronological ordering matters. LSTMs are essentially improved versions of RNNs, capable of interpreting longer sequences of data. Let's take a look at how RNNs and LSTMS are structured and how they enable the creation of sophisticated natural language processing systems. So before we talk about how Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) work, we should discuss the format of a neural network in general.

Top Stories, Tweets Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford's Law and why is it important for data science? Top KDnuggets tweets, Jul 31 - Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing News Exploratory Data Analysis Using Python Meetings The slow, startling triumph of Reverend Bayes - John Elder's 2019 Keynote at PAW in London Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme Academic Postdoctoral position (2 years) in multivariate analysis and deep learning PhD student position in computational science with focus on chemistry Monash University: Research Fellow - Computer Vision [Melbourne, Australia] Image of the week 12 NLP Researchers, Practitioners, Innovators to Follow Learn how to do Machine Learning on Graphs; Follow these 12 amazing leaders in NLP; Read the explanation of Deep Learning for NLP, including ANNs, RNNs and LSTMs; Understand what is Benford's Law and why is it important for data science; Find the 6 key concepts in Andrew NG Machine Learning Yearning; and more. Knowing Your Neighbours: Machine Learning on Graphs 12 NLP Researchers, Practitioners & Innovators You Should Be Following Deep Learning for NLP: ANNs, RNNs and LSTMs explained! What is Benford's Law and why is it important for data science?

Electricity consumption has increased exponentially during the past few decades. This increase is heavily burdening the electricity distributors. Therefore, predicting the future demand for electricity consumption will provide an upper hand to the electricity distributor. Predicting electricity consumption requires many parameters. The paper presents two approaches with one using a Recurrent Neural Network (RNN) and another one using a Long Short Term Memory (LSTM) network, which only considers the previous electricity consumption to predict the future electricity consumption. These models were tested on the publicly available London smart meter dataset. To assess the applicability of the RNN and the LSTM network to predict electricity consumption, they were tested to predict for an individual house and a block of houses for a given time period. The predictions were done for daily, trimester and 13 months, which covers short term, mid-term and long term prediction. Both the RNN and the LSTM network have achieved an average Root Mean Square error of 0.1.