We propose an approximation algorithm for efficient correlation search in time series data. In our method, we use Fourier transform and neural network to embed time series into a low-dimensional Euclidean space. The given space is learned such that time series correlation can be effectively approximated from Euclidean distance between corresponding embedded vectors. Therefore, search for correlated time series can be done using an index in the embedding space for efficient nearest neighbor search. Our theoretical analysis illustrates that our method's accuracy can be guaranteed under certain regularity conditions. We further conduct experiments on real-world datasets and the results show that our method indeed outperforms the baseline solution. In particular, for approximation of correlation, our method reduces the approximation loss by a half in most test cases compared to the baseline solution. For top-$k$ highest correlation search, our method improves the precision from 5\% to 20\% while the query time is similar to the baseline approach query time.

Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, we use variational inference to jointly learn the parameters and the diffusion paths. We use a standard mean-field variational approximation of the parameter posterior, and introduce a recurrent neural network to approximate the posterior for the diffusion paths conditional on the parameters. This neural network learns how to provide Gaussian state transitions which bridge between observations in a very similar way to the conditioned diffusion process. The resulting black-box inference method can be applied to any SDE system with light tuning requirements. We illustrate the method on a Lotka-Volterra system and an epidemic model, producing accurate parameter estimates in a few hours.

Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcomings. Our network, which we call SpectralNet, learns a map that embeds input data points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them. We train SpectralNet using a procedure that involves constrained stochastic optimization. Stochastic optimization allows it to scale to large datasets, while the constraints, which are implemented using a special-purpose output layer, allow us to keep the network output orthogonal. Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points. To further improve the quality of the clustering, we replace the standard pairwise Gaussian affinities with affinities leaned from unlabeled data using a Siamese network. Additional improvement can be achieved by applying the network to code representations produced, e.g., by standard autoencoders. Our end-to-end learning procedure is fully unsupervised. In addition, we apply VC dimension theory to derive a lower bound on the size of SpectralNet. State-of-the-art clustering results are reported on the Reuters dataset. Our implementation is publicly available at https://github.com/kstant0725/SpectralNet .

Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks. We propose a parameterization of such linear operations using functional calculus to achieve arbitrarily low learning complexity. The proposed approach is shown to model richer behaviors and display greater flexibility in learning representations than product graph methods.

Infosys Ltd says that Artificial Intelligence (AI) technologies "are already being broadly deployed, producing real results, and impacting business strategy" in at least 73% of companies across the globe. Along with other Indian IT companies, Infosys fancies itself a leader in AI. The reality, though, according to MIT Sloan Management Review and Boston Consulting Group, is that hardly one in 20 US companies has extensively incorporated AI into its offerings or processes. Businesses understand neither what AI is nor how to realize its amazing potential. The only significant player in the AI enterprise market is IBM Global Services, with IBM Watson technology, and its take-up has been slow due to these limitations. If Indian companies want a share of the emerging AI market opportunity, they need to start with a realistic understanding of what AI is and then build practices to educate their customers and transform industries.

Nearly all aspects of modern life are in some way being changed by big data and machine learning. Netflix knows what movies people like to watch and Google knows what people want to know based on their search histories. Indeed, Google has recently begun to replace much of its existing non–machine learning technology with machine learning algorithms, and there is great optimism that these techniques can provide similar improvements across many sectors. It is no surprise then that medicine is awash with claims of revolution from the application of machine learning to big health care data. Recent examples have demonstrated that big data and machine learning can create algorithms that perform on par with human physicians.1 Though machine learning and big data may seem mysterious at first, they are in fact deeply related to traditional statistical models that are recognizable to most clinicians.

So today, I will continue my journey to Bio-informatics with Machine Learning. And I will try to perform the most basic task in Bio-informatics, which is converting DNA sequence to Protein. Also, this is over complicating the task, we can just build a dictionary to map the values, as done by Vijini Mallawaarachchi in this post. Also, please take note that we are going to preprocess the DNA / Protein sequence to vectors, if you are not aware of how to do that, please see this post. Finally, I am going to perform Dilated Back Propagation to train our network.

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc. They all aim to generate specific insights from the data. In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science. This data science book is a great blend of lectures in the modern theoretical course in data science. This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning.

What do you think of when you hear about AI? Do you picture your favorite sci-fi movie or a book that you read when you were younger? In that favorite book or movie, were the robots smart? In AI, we can find a subset of machine learning called "deep learning," which is defined as networks that can learn unsupervised from unstructured data. Now the bigger question is: Are you ready to take advantage of deep learning in your business? The vast ocean of data grows exponentially every day. If you and your company can't keep up, you'll be left behind.

NEW YORK, March 12, 2018 (GLOBE NEWSWIRE) -- The Global Artificial Intelligence in Agriculture (AIA) Market is expected to grow at a significant CAGR of 24.3% during the forecast period. The factors driving the growth of the global AIA market are rising adoption of information management systems (IMS), automated irrigation, increasing crop productivity by implementing deep learning techniques, and increasing global population. Furthermore, growing trend of precision farming and increasing adoption of smart sensors are also fueling the demand of the global AIA market. Replacement of human labor is also expected to overcome by AIA, to minimize scarcity of physical labor. However, the high cost of collecting data of agricultural land is a major restraint of the AIA market growth.