output
20022025
most citedGW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

9.8k citations

Showing 2019Show all

52 papers · 1 filter

cs.HC20198 cited

Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone

Amith K. Belman, Li Wang, S. S. Iyengar +6

Behavioral biometrics are key components in the landscape of research in continuous and active user authentication. However, there is a lack of large datasets with multiple activit…

cs.CL201916 cited

Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models

Lizhen Liang, Daniel E. Acuna

Detecting biases in artificial intelligence has become difficult because of the impenetrable nature of deep learning. The central difficulty is in relating unobservable phenomena d…

math.OC201910 cited

Byzantine Resilient Non-Convex SVRG with Distributed Batch Gradient Computations

Prashant Khanduri, Saikiran Bulusu, Pranay Sharma +1

In this work, we consider the distributed stochastic optimization problem of minimizing a non-convex function in an adversarial sett…

hep-lat20192 cited

The hadronic vacuum polarization of the muon from four-flavor lattice QCD

C. T. H. Davies, C. E. DeTar, A. X. El-Khadra +16

We present an update on the ongoing calculations by the Fermilab Lattice, HPQCD, and MILC Collaboration of the leading-order (in electromagnetism) hadronic vacuum polarization cont…

cs.CR20193 cited

An Observational Investigation of Reverse Engineers' Processes

Daniel Votipka, Seth M. Rabin, Kristopher Micinski +2

Reverse engineering is a complex process essential to software-security tasks such as vulnerability discovery and malware analysis. Significant research and engineering effort has…

eess.IV20199 cited

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

Yantao Lu, Yunhan Jia, Jianyu Wang +4

Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other m…