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Found 16 papers, 2 papers with code
QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
In this setting, performing periodic model retraining with the old and new data accumulated is beneficial as it fully captures possible drifts in the data distribution over time.
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.
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.
Prospects of Privacy Advantage in Quantum Machine Learning
We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models.
Privacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes.
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Expressive variational quantum circuits provide inherent privacy in federated learning
However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.
Des-q: a quantum algorithm to provably speedup retraining of decision trees
Assuming the data stream produces small, periodic increments of new training examples, Des-q significantly reduces the tree retraining time.
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Numerical evidence against advantage with quantum fidelity kernels on classical data
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
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.
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets.
Ranked #89 on
Action Recognition
on Something-Something V2
(using extra training data)
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.
Efficient Fusion of Sparse and Complementary Convolutions
We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions.
Sparse-Complementary Convolution for Efficient Model Utilization on CNNs
By simply replacing the convolution of a CNN with our sparse-complementary convolution, at the same FLOPs and parameters, we can improve top-1 accuracy on ImageNet by 0. 33% and 0. 18% for ResNet-101 and ResNet-152, respectively.
