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Found 22 papers, 13 papers with code
Interpreting Themes from Educational Stories
Reading comprehension continues to be a crucial research focus in the NLP community.
Positive and Risky Message Assessment for Music Products
In this work, we introduce a pioneering research challenge: evaluating positive and potentially harmful messages within music products.
On-orbit model training for satellite imagery with label proportions
This work addresses the challenge of training supervised machine or deep learning models on orbiting platforms where we are generally constrained by limited on-board hardware capabilities and restricted uplink bandwidths to upload.
Kernel Density Matrices for Probabilistic Deep Learning
This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables.
What are the Machine Learning best practices reported by practitioners on Stack Exchange?
In particular, Software Engineering (SE) is one of those disciplines in which ML has been used for multiple tasks, like software categorization, bugs prediction, and testing.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models.
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
The prediction phase complexity of the proposed algorithm is constant relative to the training data size, and it performs well in data sets with different anomaly rates.
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection
This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy.
Fast Kernel Density Estimation with Density Matrices and Random Fourier Features
In this paper, we systematically evaluate the novel DMKDE algorithm and compare it with other state-of-the-art fast procedures for approximating the kernel density estimation method on different synthetic data sets.
Quantum Adaptive Fourier Features for Neural Density Estimation
Density estimation is a fundamental task in statistics and machine learning applications.
An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States
We consider the Feynman-Kitaev formalism applied to a spin chain described by the transverse field Ising model.
Optimisation-free Classification and Density Estimation with Quantum Circuits
We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits.
A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection
The kaggle EyePACS subset is used as a training set and the Messidor-2 as a test set for lesions and DR classification models.
Quantum Measurement Classification with Qudits
Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer.
Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression
Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide.
Learning with Density Matrices and Random Features
It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations.
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented.
Dissimilarity Mixture Autoencoder for Deep Clustering
The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture.
Supervised Learning with Quantum Measurements
Prediction of the label for a new sample is made by performing a projective measurement on the bipartite system with an operator, prepared from the new input sample, and applying a partial trace to obtain the state of the subsystem representing the output.
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media
Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2. 45% and 3. 69%, establishing a more suitable approach for social media environments.
Ranked #17 on
Named Entity Recognition (NER)
on WNUT 2017
Quantum Latent Semantic Analysis
LTA is a valuable technique for document analysis and representation, which has been extensively used in information retrieval and machine learning.
Gated Multimodal Units for Information Fusion
The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities.
