Exponential Machines

Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called Tensor Train (TT). The Tensor Train format regularizes the model and lets you control the number of underlying parameters. To train the model, we develop a stochastic Riemannian optimization procedure, which allows us to fit tensors with 2^160 entries. We show that the model achieves state-of-the-art performance on synthetic data with high-order interactions and that it works on par with high-order factorization machines on a recommender system dataset MovieLens 100K.Comment: ICLR-2017 workshop track pape

Percolation of three fluids on a honeycomb lattice

In this paper, we consider a generalization of percolation: percolation of three related fluids on a honeycomb lattice. K. Izyurov and A. Magazinov proved that percolations of distinct fluids between opposite sides on a fixed hexagon become mutually independent as the lattice step tends to 0. This paper exposes this proof in details (with minor simplifications) for nonspecialists. In addition, we state a few related conjectures based on numerical experiments.Comment: in English and in Russian; 15 page

Remote Sensing of Neutron and Gamma Radiation using Aerial Unmanned Autonomous System

With the continuing advancement of nuclear technologies, the detection and identification of radioactive material is a necessary part of commercial and government applications. There is a wide array of options available for detection and identification of material, but most rely on compact devices which are manually positioned. The deployment of robots equipped with detection equipment is not always feasible, especially in locations where there is considerable debris on the ground, or where there are low clearance areas. To solve this, the goal of this research was to design a remote sensing system for radiation using unmanned aerial vehicles (UAVs). A swarm of small-scale quadcopters with detection and navigation capabilities were employed to carry out dynamically tracked radiation measurements. Detection was carried out though the use of a Cs2LiYCl6:Ce3+ scintillation detector equipped with pulse shape discrimination (PSD). This allowed for differentiation between neutron and photon radiation signatures based on the shape of the signal. The maximum likelihood estimation technique was employed to search remotely for radiation sources using the data obtained by multiple UAVs

Prediction of Metal Sample Failure from Scanning Electron Microscope images using Deep Learning Neural Network

We present the preliminary results on using a deep learning neural network to predict a metal sample failure based on a set of images obtained with a Scanning Electron Microscope. Various metal alloy samples were prepared according to ASTM E8/E8M-11 standards for a tensile test. Each sample was prepared for circle grid analysis and then stressed on a tensile machine. Stress and strain values were obtained for each position along the sample by measuring dimensions of each elongated circle. Increasing stress and strain values were found closer to the breakage of the sample with low values found at the holding positions of the sample. Thus, each sample provided a scale of stress/strain values. The correlated stress and strain values were used to determine a failure likelihood for the sample at each position. Multiple SEM images of the failed steel sample were taken at various locations. SEM images were taken using, Large-Chamber SEM (LC-SEM) at the NOVA Center, WKU. All SEM images were tagged with previously obtained stress and strain values for each circle, correlating position of the circle and position of the image. Using obtained set of SEM images, we trained neural network to classify SEM images based on their stress/strain values. The predicted values were used to analyze the failure likelihood of each sample

MLIP-3: Active learning on atomic environments with Moment Tensor Potentials

Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer codes developed within the community. In the field of atomistic modeling these were software packages for classical atomistic modeling, later -- quantum-mechanical modeling, and now with the fast growth of the field of machine-learning potentials, the packages implementing such potentials. In this paper we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package (Novikov et al. (2020), The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.), however with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation