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Found 11 papers, 1 papers with code
DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection
In DeepDRK, a generative model grounded in a transformer architecture is introduced to better achieve the "swap property".
Learning Personalized Representations using Graph Convolutional Network
Then we propose a graph convolutional network(GCN) based model, namely Personalized Dynamic Routing Feature Encoder(PDRFE), that generates personalized customer representations learned from the built graph.
Improving Generalizability of Protein Sequence Models via Data Augmentations
While protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the protein's function.
Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging
In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc.
Statistically-informed deep learning for gravitational wave parameter estimation
Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1, m_2, a_f, \omega_R, \omega_I)$ of five binary black holes: $\texttt{GW150914}, \texttt{GW170104}, \texttt{GW170814}, \texttt{GW190521}$ and $\texttt{GW190630}$.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Gravitational wave signals are often extremely weak and the data from the detectors, such as LIGO, is contaminated with non-Gaussian and non-stationary noise, often containing transient disturbances which can obscure real signals.
Glitch Classification and Clustering for LIGO with Deep Transfer Learning
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise.
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
The exquisite sensitivity of the advanced LIGO detectors has enabled the detection of multiple gravitational wave signals.
