276 research outputs found
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine
learning research, as multiple information sources co-occur when describing
natural phenomena. However, existing generative models that approximate a
multimodal ELBO rely on difficult or inefficient training schemes to learn a
joint distribution and the dependencies between modalities. In this work, we
propose a novel, efficient objective function that utilizes the Jensen-Shannon
divergence for multiple distributions. It simultaneously approximates the
unimodal and joint multimodal posteriors directly via a dynamic prior. In
addition, we theoretically prove that the new multimodal JS-divergence (mmJSD)
objective optimizes an ELBO. In extensive experiments, we demonstrate the
advantage of the proposed mmJSD model compared to previous work in
unsupervised, generative learning tasks.Comment: Accepted at NeurIPS 2020, camera-ready versio
Part of Speech Based Term Weighting for Information Retrieval
Automatic language processing tools typically assign to terms so-called
weights corresponding to the contribution of terms to information content.
Traditionally, term weights are computed from lexical statistics, e.g., term
frequencies. We propose a new type of term weight that is computed from part of
speech (POS) n-gram statistics. The proposed POS-based term weight represents
how informative a term is in general, based on the POS contexts in which it
generally occurs in language. We suggest five different computations of
POS-based term weights by extending existing statistical approximations of term
information measures. We apply these POS-based term weights to information
retrieval, by integrating them into the model that matches documents to
queries. Experiments with two TREC collections and 300 queries, using TF-IDF &
BM25 as baselines, show that integrating our POS-based term weights to
retrieval always leads to gains (up to +33.7% from the baseline). Additional
experiments with a different retrieval model as baseline (Language Model with
Dirichlet priors smoothing) and our best performing POS-based term weight, show
retrieval gains always and consistently across the whole smoothing range of the
baseline
Prédire la difficulté des requêtes : la combinaison de mesures statistiques et sémantiques
National audienceLa performance d’un Système de Recherche d’Information (SRI) est étroitement liée à la requête. Les requêtes pour lesquelles les SRI échouent sont appelées dans la littérature des « requêtes difficiles ». L’étude présentée dans cet article vise à analyser, adapater et combiner plusieurs prédicteurs de difficulté de requêtes. Nous avons considéré trois prédicteurs: un lié à l’ambiguïté des termes, un basé sur la fréquence des termes et une mesure de répartition des résultats. L’évaluation de la prédiction est basée sur la corrélation entre la difficulté prédite et la performance réelle des SRI. Nous montrons que la combinaison de ces prédicteurs donne de bons résultats. Le cadre d’évaluation est celui des collections TREC7 et TREC8 adhoc
Using the Jensen-Shannon, density power, and Itakura-Saito divergences to implement an evolutionary-based global localization filter for mobile robots
One of the most demanding skills for a mobile robot is to be intelligent enough to know its own location. The global localization problem consists of obtaining the robot's pose (position and orientation) in a known map if the initial location is unknown. This task is addressed applying evolutionary computation concepts (Differential Evolution). In the current approach, the distances obtained from the laser sensors are combined with the predicted scan (in the known map) from possible locations to implement a cost function that is optimized by an evolutionary filter. The laser beams (sensor information) are modeled using a combination of probability distributions to implement a non-symmetric fitness function. The main contribution of this paper is to apply the probabilistic approach to design three different cost functions based on known divergences (Jensen-Shannon, Itakura-Saito, and density power). The three metrics have been tested in different experiments and the localization module performance is exceptional in regions with occlusions caused by different obstacles. This fact validates that the non-symmetric probabilistic approach is a suitable technique to be applied to multiple metrics.This work was supported by the Competitive Improvement of Drilling and Blasting Cycle in Mining and Underground-Works through New Techniques of Engineering, Explosives, Prototypes, and Advanced Tools, which is a Research and Development project undertaken by the following companies: Obras Subterr a neas, MaxamCorp Holding, Putzmeister Iberica, Subterra Ingenieria, Expace On Boards Systems, Dacartec Servicios Informaticos, and Cepasa Ensayos Geotecnicos
iQPP: A Benchmark for Image Query Performance Prediction
To date, query performance prediction (QPP) in the context of content-based
image retrieval remains a largely unexplored task, especially in the
query-by-example scenario, where the query is an image. To boost the
exploration of the QPP task in image retrieval, we propose the first benchmark
for image query performance prediction (iQPP). First, we establish a set of
four data sets (PASCAL VOC 2012, Caltech-101, ROxford5k and RParis6k) and
estimate the ground-truth difficulty of each query as the average precision or
the precision@k, using two state-of-the-art image retrieval models. Next, we
propose and evaluate novel pre-retrieval and post-retrieval query performance
predictors, comparing them with existing or adapted (from text to image)
predictors. The empirical results show that most predictors do not generalize
across evaluation scenarios. Our comprehensive experiments indicate that iQPP
is a challenging benchmark, revealing an important research gap that needs to
be addressed in future work. We release our code and data as open source at
https://github.com/Eduard6421/iQPP, to foster future research.Comment: Accepted at SIGIR 202
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
This thesis is in the area called computational social choice which is an
intersection area of algorithms and social choice theory.Comment: Ph.D. Thesi
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