arXiv research

This study examines how imbalanced training data affects author name disambiguation.

A CNN for lidar data improves understanding of moving vehicles.

Paper develops neural network for Mandarin polyphone disambiguation.

EviTrack improves sequential prediction in delayed disambiguation scenarios.

Paper proposes an algorithm to recover full supervision from weakly labeled data.

Model learns to discover and disambiguate entities and relations in text streams.

A new system learns entity representations to improve local entity disambiguation.

A new method for name disambiguation in academic networks using multi-view attention and recurrent neural networks.

Improves medical note processing by training model on related concepts and global context.

A coloring scheme improves graph neural networks for node disambiguation.

This study improves author disambiguation without supervision using feature overlap.

Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…

DivDis learns diverse hypotheses from underspecified data to improve robustness.

Framework tracks evolving news stories across multiple sources.

New method clusters unknown music artists using audio metrics.

Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold appr…

Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.

Paper proposes FOFE for efficient WSD.

PML-GAN tackles noisy multi-label annotations using adversarial learning.

Efficient autoregressive entity linking with correction for faster, more accurate results.

DKPCA improves WSD accuracy with scarce labeled data.

Pangloss improves entity linking in noisy text environments.

FONDUE identifies ambiguous nodes in networks for better analysis.

Improves biomedical entity linking with latent type modeling.

Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers…

The study quantifies uncertainty to improve model calibration and disambiguate annotator and data bias in emotion recognition.

Active inference selects actions to maximize information gain, aiding structure learning.

AXE evaluates explanations to avoid misleading Rashomon set model selection.

We present an LDA approach to entity disambiguation. Each topic is associated with a Wikipedia article and topics generate either content words or entity mentions. Training such models is challenging because of the topic and vocabulary size, both in the millions. We tackle these problems using a novel distributed infer…

MT-VAE learns motion transitions for generating diverse future motions.

Introduces Motion Programs for better video analysis of human motion.

We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution…

Study motion planning for points avoiding obstacles in a plane.

Paper introduces new motion synthesis model using normalizing flows.

Programmatic Motion Concepts learn human actions from paired videos.

Unified framework for human motion generation on Riemannian manifolds.

Study on determinants of unitary Brownian motion and their asymptotic laws.

New framework predicts diverse, contextually plausible 3D human motions.

In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the associati…

Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this …

Study refracted skew Brownian motion, find densities and asymptotics.

Neural network predicts vessel motions with high accuracy.

Study fractal dimension for motion without crossing a subset.

Let $E$ be a closed set in the Riemann sphere $\widehat{\mathbb{C}}$. We consider a holomorphic motion $φ$ of $E$ over a complex manifold $M$, that is, a holomorphic family of injections on $E$ parametrized by $M$. It is known that if $M$ is the unit disk $Δ$ in the complex plane, then any holomorphic motion of $E$ ove…

Neural network estimates rigid motion in stroke imaging to improve image quality.

Study cohomological equation for robotic screw motions on SE(3).

Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales. We demonstrate a robust and scalable SPCA algorithm b…

New approach for obstacle avoidance in robotics using learned representations.