21 citations · 59 across the 12 of their papers we have counts for
12 papers
Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
The flatness hypothesis suggests that flatness of the loss landscape, as measured by the eigenvalues of the loss Hessian, correlates with better neural network generalization. Whil…
Wavelet-Based Extraction of Transient Noise in Gravitational-Wave Interferometers using a Saliency-Guided Learning Architecture
Christopher Allene, Dhruv Kumar, Yusuke Sakai +2
Gravitational-wave interferometers exhibit a wide variety of short-duration non-Gaussian transients, commonly referred to as glitches, that complicate the detection of astrophysica…
Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
Neural networks (NNs) are central to modern machine learning and achieve state-of-the-art results in many applications. However, the relationship between loss geometry and generali…
Glitch noise classification in KAGRA O3GK observing data using unsupervised machine learning
Shoichi Oshino, Yusuke Sakai, Marco Meyer-Conde +5
Gravitational wave interferometers are disrupted by various types of nonstationary noise, referred to as glitch noise, that affect data analysis and interferometer sensitivity. The…
Regression of Suspension Violin Modes in KAGRA O3GK Data with Kalman Filters
Lucas Moisset, Marco Meyer-Conde, Christopher Allene +4
Suspension thermal modes in interferometric gravitational-wave detectors produce narrow, high-Q spectral lines that can contaminate gravitational searches and bias parameter estima…
Gravitational Wave Memory from Accelerating Relativistic Jets in Multiple Thick Shell Scenarios
Yusuke Sakai, Ryo Yamazaki, Yoshihiro Okutani +4
Gravitational wave (GW) memory, a permanent distortion of the space-time metric, is anticipated during the acceleration of relativistic jets in gamma-ray bursts (GRBs). While the p…
Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform
Seiya Sasaoka, Yusuke Sakai, Diego Dominguez +5
Core-collapse supernovae (CCSNe) are potential multimessenger events detectable by current and future gravitational wave (GW) detectors. The GW signals emitted during these events…
Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
Seiya Sasaoka, Naoki Koyama, Diego Dominguez +4
Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This stu…
Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae
Seiya Sasaoka, Naoki Koyama, Diego Dominguez +4
In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived…
FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks
Yuto Omae, Yusuke Sakai, Hirotaka Takahashi
Recently, many convolutional neural networks (CNNs) for classification by time domain data of multisignals have been developed. Although some signals are important for correct clas…
Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset
Yusuke Sakai, Yousuke Itoh, Piljong Jung +11
Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-…
Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors
Yusuke Sakai, Yousuke Itoh, Piljong Jung +11
In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results…