Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, the deep neural network has advantages in handling high dimensional data. In view that a differentiable neural decision forest can be added to the neural network to fully exploit the benefits of both models, in our work, we further combine convolutional autoencoder with neural decision forest, where autoencoder has its advantages in finding the hidden representations of the input data. We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance. The idea of gradient boost is to learn and use the residual in the prediction. In addition, we design a structure to learn the parameters of the neural decision forest and gradient boost module at contiguous steps. The extensive experiments on several public datasets demonstrate that our proposed model achieves good efficiency and prediction performance compared with a series of baseline methods.

Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.

Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination requires classifying the manifolds in a manner that is insensitive to variability within a manifold. How neuronal systems give rise to invariant object classification and recognition is a fundamental problem in brain theory as well as in machine learning. Here we study the ability of a readout network to classify objects from their perceptual manifold representations. We develop a statistical mechanical theory for the linear classification of manifolds with arbitrary geometry revealing a remarkable relation to the mathematics of conic decomposition. Novel geometrical measures of manifold radius and manifold dimension are introduced which can explain the classification capacity for manifolds of various geometries. The general theory is demonstrated on a number of representative manifolds, including L2 ellipsoids prototypical of strictly convex manifolds, L1 balls representing polytopes consisting of finite sample points, and orientation manifolds which arise from neurons tuned to respond to a continuous angle variable, such as object orientation. The effects of label sparsity on the classification capacity of manifolds are elucidated, revealing a scaling relation between label sparsity and manifold radius. Theoretical predictions are corroborated by numerical simulations using recently developed algorithms to compute maximum margin solutions for manifold dichotomies. Our theory and its extensions provide a powerful and rich framework for applying statistical mechanics of linear classification to data arising from neuronal responses to object stimuli, as well as to artificial deep networks trained for object recognition tasks.

You don't have to write even a single line of code to win this competition. So for all those who knows the concept part but are not from a coding background can finally not only participate but also win it and for others they finally don't have to worry whether the code will compile or not? Today soda bottle companies send a person to stores to find out if they need to restock some soda bottles that have run out on weekly basis. There are 100s of thousands of these coolers at different stores all across the world. This takes large amount of human labor in travel and counting bottles.

We believe that current computing solutions don't stack up for running neural networks (i.e., deep learning) at scale in resource-constrained environments,

Good results come from a brain-inspired system that uses the IBM TrueNorth neuromorphic chips together with a pair of vision sensors that act like eyes. Together the system can respond to changes in the environment to provide imagery in stereo with a sense of depth. Simply put, they're able to hone in on the action, and ignore extraneous visual noise. In the research paper "A Low Power, High Throughput, Fully Event-Based Stereo System" the team reported results of 200x less power per pixel than a comparable DVS system while achieving competitive accuracies.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We also provide pre-trained models for several benchmark translation datasets. If you use Docker make sure to increase the shared memory size either with --ipc host or --shm-size as command line options to nvidia-docker run. This model uses a Byte Pair Encoding (BPE) vocabulary, so we'll have to apply the encoding to the source text before it can be translated. This can be done with the apply_bpe.py

A deep learning neural network trained on 50,000 crystal structures of inorganic materials has acquired the ability to recognise chemical similarities and predict new materials. One way to find out whether two elements from the periodic table will form a crystalline material is the tried and trusted'shake and bake' – mix them together at a range of different stoichiometries and hope for the best. Binary materials are thus very well covered in the scientific literature, but this method can't keep up with the vastly more complex combinatorial possibilities afforded by three or more elements. Therefore, predictions of which elements will combine in which ratios to form regular solids are necessary and they hold the promise of new materials with desirable or even unprecedented properties. Current prediction methods typically use evolutionary algorithms and apply them to random starting structures.

Today at the Computer Vision and Pattern Recognition Conference in Salt Lake City, Utah, NVIDIA is kicking off the conference by demonstrating an early release of Apex, an open-source PyTorch extension that helps users maximize deep learning training performance on NVIDIA Volta GPUs. Inspired by state-of-the-art mixed precision training in translational networks, sentiment analysis, and image classification, NVIDIA PyTorch developers have created tools bringing these methods to all levels of PyTorch users. Mixed precision utilities in Apex are designed to improve training speed while maintaining the accuracy and stability of training in single precision. Specifically, Apex offers automatic execution of operations in either FP16 or FP32, automatic handling of master parameter conversion, and automatic loss scaling, all available with 4 or fewer line changes to the existing code. Installation requires CUDA 9, PyTorch 0.4 or later, and Python 3. The modules and utilities are still under active development and we look forward to your feedback to make these utilities even better.

Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves will teach you Deep Learning with Artificial Neural Networks. You will work with Tensorflow and Pytorch to build several different types of Neural Networks. Data Science: Deep Learning in Python by Lazy Programmer Inc. will teach you build Neural Networks from scratch in Python, numpy & TensorFlow. You will learn about the various types and terms associated to neural networks. Natural Language Processing with Deep Learning in Python by Lazy Programmer Inc. will teach you everything about deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.