At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

We are looking for a Machine Learning Researcher with a specialised focus on Reinforcement and Active Learning. The candidate will have a sound understanding of modern machine learning, deep learning, probabilistic modelling techniques and expertise in Reinforcement and Active Learning and their applications in real-world problems. You will have the opportunity to contribute to this high performing team who seek to apply their knowledge in the high impact field of improving human's capability in drug discovery. If this challenge and opportunity excites you, please email your CV and a covering letter to careers@benevolent.ai

Editor's note: Please note that, while this chart and post was up to date when it was first published, the landscape has changed in such a way that the table below is not depict a fully accurate picture at this point (e.g. Keras now supports a greater number of frameworks). The post is still beneficial, however, with this caveat noted. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance.

Blocks, a Theano framework for training neural networks Caffe, a deep learning framework made with expression, speed, and modularity in mind. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. ConvNet, a Matlab based convolutional neural network toolbox - a type of deep learning, can learn useful features from raw data by itself.

Welcome to the Semantic Web - Chris Skinner's blog. Vincent Fournier/Gallerystock By Toby Walsh However you look at it, the future appears bleak. The world is under immense stress environmentally, economically and politically. The novelist who inspired Elon Musk. Elon Musk, the world's most restless entrepreneur, has embarked on yet another venture.

At first blush, Scot Barton might not seem like an AI pioneer. He isn't building self-driving cars or teaching computers to thrash humans at computer games. But within his role at Farmers Insurance, he is blazing a trail for the technology. Barton leads a team that analyzes data to answer questions about customer behavior and the design of different policies. His group is now using all sorts of cutting-edge machine-learning techniques, from deep neural networks to decision trees.

A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting "learned" DBP significantly reduces the complexity compared to conventional DBP implementations.

We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such models using inference (or recognition) networks; we identify a crucial problem when modeling large, sparse, high-dimensional datasets -- underfitting. We study the extent of underfitting, highlighting that its severity increases with the sparsity of the data. We propose methods to tackle it via iterative optimization inspired by stochastic variational inference \citep{hoffman2013stochastic} and improvements in the sparse data representation used for inference. The proposed techniques drastically improve the ability of these powerful models to fit sparse data, achieving state-of-the-art results on a benchmark text-count dataset and excellent results on the task of top-N recommendation.

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the model during training. The proposed method can be considered an unusual form of regularization: the model parameters are obtained by training a relatively low-capacity model, that is relatively easy to optimize at the beginning, with only a few iterations, and these parameters are reused for the initialization of a higher-capacity model. We derive the upper and lower bounds of variance of the weight variation, and show that the initial symmetric structure of NGs helps stabilize training. We evaluate the proposed method on different frameworks of convolutional neural networks over two object recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results showed that the proposed method allows us to (1) speed up the convergence of training, (2) allow for less careful weight initialization, (3) improve or at least maintain the performance of the model at negligible extra computational cost, and (4) easily train a very deep model.

We propose a family of near-metrics based on local graph diffusion to capture similarity for a wide class of data sets. These quasi-metametrics, as their names suggest, dispense with one or two standard axioms of metric spaces, specifically distinguishability and symmetry, so that similarity between data points of arbitrary type and form could be measured broadly and effectively. The proposed near-metric family includes the forward k-step diffusion and its reverse, typically on the graph consisting of data objects and their features. By construction, this family of near-metrics is particularly appropriate for categorical data, continuous data, and vector representations of images and text extracted via deep learning approaches. We conduct extensive experiments to evaluate the performance of this family of similarity measures and compare and contrast with traditional measures of similarity used for each specific application and with the ground truth when available. We show that for structured data including categorical and continuous data, the near-metrics corresponding to normalized forward k-step diffusion (k small) work as one of the best performing similarity measures; for vector representations of text and images including those extracted from deep learning, the near-metrics derived from normalized and reverse k-step graph diffusion (k very small) exhibit outstanding ability to distinguish data points from different classes.