Lightweight learning from label proportions on satellite imagery

This work addresses the challenge of producing chip level predictions on satellite imagery when only label proportions at a coarser spatial geometry are available, typically from statistical or aggregated data from administrative divisions (such as municipalities or communes). This kind of tabular data is usually widely available in many regions of the world and application areas and, thus, its exploitation may contribute to leverage the endemic scarcity of fine grained labelled data in Earth Observation (EO). This can be framed as a Learning from Label Proportions (LLP) problem setup. LLP applied to EO data is still an emerging field and performing comparative studies in applied scenarios remains a challenge due to the lack of standardized datasets. In this work, first, we show how simple deep learning and probabilistic methods generally perform better than standard more complex ones, providing a surprising level of finer grained spatial detail when trained with much coarser label proportions. Second, we provide a set of benchmarking datasets enabling comparative LLP applied to EO, providing both fine grained labels and aggregated data according to existing administrative divisions. Finally, we argue how this approach might be valuable when considering on-orbit inference and training. Source code is available at https://github.com/rramosp/llpeoComment: 16 pages, 13 figure

Quantum Kernel Mixtures for Probabilistic Deep Learning

This paper presents a novel approach to probabilistic deep learning (PDL), quantum kernel mixtures, derived from the mathematical formalism of quantum density matrices, which provides a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. The framework allows for the construction of differentiable models for density estimation, inference, and sampling, enabling integration into end-to-end deep neural models. In doing so, we provide a versatile representation of marginal and joint probability distributions that allows us to develop a differentiable, compositional, and reversible inference procedure that covers a wide range of machine learning tasks, including density estimation, discriminative learning, and generative modeling. We illustrate the broad applicability of the framework with two examples: an image classification model, which can be naturally transformed into a conditional generative model thanks to the reversibility of our inference procedure; and a model for learning with label proportions, which is a weakly supervised classification task, demonstrating the framework's ability to deal with uncertainty in the training samples

Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection

Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, these models need a large number of annotated images. In the medical domain, annotations from experts are costly, tedious, and time-consuming; as a result, a limited number of annotated images are available. This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy. The proposed method uses unsupervised pretraining via self-supervised learning followed by supervised fine-tuning with a small set of labeled images and knowledge distillation to increase the performance in classification task. This method was evaluated on the EyePACS test and Messidor-2 dataset achieving 0.94 and 0.89 AUC respectively using only 2% of EyePACS train labeled images

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. We initiate by setting a multi-faceted, multi-task benchmark for music content assessment. Subsequently, we introduce an efficient multi-task predictive model fortified with ordinality-enforcement to address this challenge. Our findings reveal that the proposed method not only significantly outperforms robust task-specific alternatives but also possesses the capability to assess multiple aspects simultaneously. Furthermore, through detailed case studies, where we employed Large Language Models (LLMs) as surrogates for content assessment, we provide valuable insights to inform and guide future research on this topic. The code for dataset creation and model implementation is publicly available at https://github.com/RiTUAL-UH/music-message-assessment.Comment: Accepted at LREC-COLING 2024 (long paper

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. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. The quantum state of the arbitrarily large training data set summarises its probability distribution in a finite-dimensional quantum wave function. By projecting the quantum state of a new data sample onto the quantum state of the training data set, one can derive statistics to classify or estimate the density of the new data sample. Remarkably, the implementation of our framework on a real quantum device does not require any optimisation of quantum circuit parameters. Nonetheless, we discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.Comment: Paper condensing experiments shown in QTML 202

Supervised Learning with Quantum Measurements

This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. Specifically, the relationship between input and output variables is represented as the state of a bipartite quantum system. The state is estimated from training samples through an averaging process that produces a density matrix. 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. The method can be seen as a generalization of Bayesian inference classification and as a type of kernel-based learning method. One remarkable characteristic of the method is that it does not require learning any parameters through optimization. We illustrate the method with different 2-D classification benchmark problems and different quantum information encodings.Comment: Supplementary material integrated into main text. Typos correcte

Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media

Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. 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.Comment: NAACL 201

Computing rotational energy transfers of OD−/OH− in collisions with Rb: isotopic effects and inelastic rates at cold ion-trap conditions

[EN]We report close-coupling (CC) quantum dynamics calculations for collisional excitation/de-excitation of the lowest four rotational levels of OD− and of OH− interacting with Rb atoms in a cold ion trap. The calculations are carried out over a range of energies capable of yielding the corresponding rates for state-changing events over a rather broad interval of temperatures which cover those reached in earlier cold trap experiments. They involved sympathetic cooling of the molecular anion through a cloud of laser-cooled Rb atoms, an experiment which is currently being run again through a Heidelberg–Innsbruck collaboration. The significance of isotopic effects is analysed by comparing both systems and the range of temperatures examined in the calculations is extended up to 400 K, starting from a few mK. Both cross sections and rates are found to be markedly larger than in the case of OD−/OH− interacting the He atoms under the same conditions, and the isotopic effects are also seen to be rather significant at the energies examined in the present study. Such findings are discussed in the light of the observed trap losses of molecular anions

Cómo adaptar un modelo de aprendizaje profundo a un nuevo dominio: el caso de la extracción de relaciones biomédicas

In this article, we study the relation extraction problem from Natural Language Processing (NLP) implementing a domain adaptation setting without external resources. We trained a Deep Learning (DL) model for Relation Extraction (RE), which extracts semantic relations in the biomedical domain. However, can the model be applied to different domains? The model should be adaptable to automatically extract relationships across different domains using the DL network. Completely training DL models in a short time is impractical because the models should quickly adapt to different datasets in several domains without delay. Therefore, adaptation is crucial for intelligent systems, where changing factors and unanticipated perturbations are common. In this study, we present a detailed analysis of the problem, as well as preliminary experimentation, results, and their evaluation.En este trabajo estudiamos el problema de extracción de relaciones del Procesamiento de Lenguaje Natural (PLN). Realizamos una configuración para la adaptación de dominio sin recursos externos. De esta forma, entrenamos un modelo con aprendizaje profundo (DL) para la extracción de relaciones (RE). El modelo permite extraer relaciones semánticas para el dominio biomédico. Sin embargo, ¿El modelo puede ser aplicado a diferentes dominios? El modelo debería adaptarse automáticamente para la extracción de relaciones entre diferentes dominios usando la red de DL. Entrenar completamente modelos DL en una escala de tiempo corta no es práctico, deseamos que los modelos se adapten rápidamente de diferentes conjuntos de datos con varios dominios y sin demora. Así, la adaptación es crucial para los sistemas inteligentes que operan en el mundo real, donde los factores cambiantes y las perturbaciones imprevistas son habituales. En este artículo, presentamos un análisis detallado del problema, una experimentación preliminar, resultados y la discusión acerca de los resultados