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Found 9 papers, 7 papers with code
MAQA: A Quantum Framework for Supervised Learning
In its general formulation, MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions, such as ensemble algorithms and neural networks.
Quantum Splines for Non-Linear Approximations
Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance.
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms.
OptAGAN: Entropy-based finetuning on text VAE-GAN
We benchmark the results of the VAE-GAN model, and show the improvements brought by our RL finetuning on three widely used datasets for text generation, with results that greatly surpass the current state-of-the-art for the quality of the generated texts.
Quantum Algorithm for Ensemble Learning
The idea of ensemble learning is to build a prediction model by combining the strengths of a collection of simpler base models.
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use.
Ranked #1 on
Supervised Image Retrieval
on CIFAR-10
Quantum Ensemble for Classification
We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models.
A Variational Algorithm for Quantum Neural Networks
Quantum Computing leverages the laws of quantum mechanics to build computers endowed with tremendous computing power.
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning.
