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Found 11 papers, 2 papers with code
MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective
The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies.
Model-Agnostic Utility-Preserving Biometric Information Anonymization
The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabling a wide range of applications including authentication, health monitoring, or much more sophisticated analytics.
Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation
Predictive multiplicity refers to the phenomenon in which classification tasks may admit multiple competing models that achieve almost-equally-optimal performance, yet generate conflicting outputs for individual samples.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
MaSS: Multi-attribute Selective Suppression
The recent rapid advances in machine learning technologies largely depend on the vast richness of data available today, in terms of both the quantity and the rich content contained within.
Quantum Machine Learning for Finance
In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term.
Road Grade Estimation Using Crowd-Sourced Smartphone Data
Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps.
A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest.
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain.
Multi-scale Neural Networks for Retinal Blood Vessels Segmentation
Existing supervised approaches didn't make use of the low-level features which are actually effective to this task.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice.
