72 citations · 184 across the 11 of their papers we have counts for
11 papers
Phase transitions of eutectic high entropy alloy AlCoCrFeNi2.1 under shock compression
Sophie E. Parsons, Kento Katagiri, Hangman Chen +31
High entropy alloys (HEAs) are a new class of metals that exhibit unique mechanical performance. Among HEAs, additively manufactured eutectic high entropy alloys (AM-EHEAs) have re…
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori +3
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the major…
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Kota Nakamura, Yasuko Matsubara, Koki Kawabata +3
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and…
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing
Yuma Ichikawa, Akira Nakagawa, Hiromoto Masayuki +1
Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods using a machine learning model. With latent generative models,…
Pushing the limits of flow strength in diamond
Anirudh Hari, Kento Katagiri, Wanghui Li +34
Extreme pressures and temperatures create conditions that allow even hard and brittle materials to deform plastically. Despite extensive research, the upper limits of flow strength…
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
Théo Lacombe, Yuichi Ike, Mathieu Carriere +3
Although neural networks are capable of reaching astonishing performances on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can…
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
Martin Royer, Frédéric Chazal, Clément Levrard +2
Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning framework…
Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
Meryll Dindin, Yuhei Umeda, Frederic Chazal
This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of…
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Mathieu Carrière, Frédéric Chazal, Yuichi Ike +3
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications…
DTM-based Filtrations
Hirokazu Anai, Frédéric Chazal, Marc Glisse +4
Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Cech or Vietoris-Rips filtrations, are…
Equation of state and optical properties of shock-compressed C:H:N:O molecular mixtures
M. Guarguaglini, J. -A. Hernandez, T. Okuchi +14
Water, ethanol, and ammonia are the key components of the mantles of Uranus and Neptune. To improve structure and evolution models and give an explanation of the magnetic fields an…