Algorithm calculates incomes for Chilean pass-through entities.
problem Computing incomes for Chilean pass-through entities with complex networks.
method Markov chains and decomposition algorithm.
result There is a unique way to distribute incomes to taxpayers.
The paper proves that certain geodesics pass through timelike poles on specific Lorentzian 2-tori.
problem Existence of closed timelike geodesics through timelike poles on class A Lorentzian 2-tori.
method Study of isometries on globally hyperbolic planes to prove the existence of geodesics.
result Existence of closed timelike geodesics through timelike poles in the interior of the stable time cone.
A model for entity relatedness considering time and context.
problem Time and context influence entity relatedness.
method Flexible model using time-aware and corpus-specific word embeddings.
result The model generates accurate entity relatedness lists.
A new system learns entity representations to improve local entity disambiguation.
problem Local entity disambiguation in text.
method Entity-ELMo (E-ELMo) approach for contextual entity representation.
result Outperforms state-of-the-art models by 0.5% on AIDA test-b.
GENRE retrieves entities autoregressively, improving efficiency and accuracy.
problem Retrieving entities from queries efficiently and accurately.
method Autoregressive generation of entity names, reducing memory footprint and improving context encoding.
result Significantly improved performance on entity disambiguation, linking, and retrieval tasks.
Improves biomedical entity linking with latent type modeling.
problem Lack of fine-grained type information for entity disambiguation.
method Jointly models entity disambiguation and latent type learning without direct supervision.
result Significant performance improvements over state-of-the-art techniques.
Neural model for dynamic entity relatedness ranking.
problem Dynamic entity relationships in real-world applications.
method A neural network-based approach leveraging collective attention as supervision.
result Our model achieves better results than competitive baselines on large-scale datasets.
Improves cross-lingual NER by projecting entities from one language to another.
problem Limited annotated corpora for named entity recognition in many languages.
method Uses machine translation twice: first for sentences, then for entities; matches based on ortho- and phonetic similarity; identifies matches using distributional statistics.
result Improves cross-lingual NER by an average of 4.1 points on 5 diverse languages.
Survey on ML advances for personalized prediction considering entity characteristics.
problem Sub-optimal performance in personalized prediction due to heterogeneity in data across entities.
method Organized current literature on entity-aware modeling based on characteristics availability and training data amount.
result Recent innovations in uncertainty quantification, fairness, and knowledge-guided machine learning can improve entity-aware modeling.
Neural production systems learn visual dynamics by applying rule templates to entities.
problem Modeling interactions among entities in structured visual environments.
method Inspired by production systems, the paper uses rule templates to bind placeholder variables to specific entities, scoring and applying the best fitting rules to update entity properties.
result The architecture achieves robust future-state prediction and extrapolation from simple to complex environments, outperforming GNNs.
Study benchmarks embedding-based entity alignment methods for KGs.
problem Align entities across different KGs using embeddings.
method Survey and categorize 23 embedding-based methods, propose new KG sampling algorithm, develop open-source library.
result Understood strengths and limitations of embedding-based methods.
NeuroNER simplifies ANN-based NER for non-experts.
problem Challenging use of ANNs for NER by non-experts.
method Graphical web-based user interface for easy annotation, training, and prediction of entities.
result NeuroNER streamlines NER process for non-expert users.
Study curves on a Whitney umbrella using geometric invariants.
problem Analyzing curves passing through a specific geometric shape.
method Using Darboux frame and Frenet-Serre formulas, define invariants and investigate their properties.
result Identified degrees of divergence and top-terms of invariants.
A multi-task learning framework improves BioNER performance across different entity types.
problem Limited performance of BioNER systems due to lack of training data for each entity type.
method Multi-task learning framework that collectively uses training data of different entity types.
result Substantially better performance on 15 benchmark BioNER datasets compared to state-of-the-art systems.
OP3 models entities for better task generalization in reinforcement learning.
problem Generalizing to unseen physical tasks with combinatorial complexity.
method Object-centric perception, prediction, and planning (OP3) framework.
result OP3 outperforms oracle models and state-of-the-art video prediction models.
ME2Vec learns medical entity vectors from EHR data.
problem Learning structured information from EHR data.
method Graph-based medical entity embedding using diverse graph embedding techniques.
result ME2Vec outperforms baselines in disease diagnosis prediction.
mGENRE improves multilingual entity linking with autoregressive sequence prediction.
problem Multilingual Entity Linking (MEL) task of resolving language-specific mentions to a multilingual Knowledge Base.
method Autoregressive sequence-to-sequence system that cross-encodes mention strings and entity names.
result Over 50% improvement in average accuracy in zero-shot settings.
The paper tackles entity induction and reference in visual storytelling.
problem Coherence in visual storytelling through proper entity introduction and reference.
method Building an entity skeleton, using an encoder-decoder framework, and proposing a glocal hierarchical attention model.
result The proposed models outperform the baseline in automatic evaluation metrics and human preference.
This research evaluates and introduces new heuristics for clustering Bitcoin blockchain entities.
problem Efficiently analyzing the vast number of Bitcoin blockchain entities.
method Examined and introduced four new heuristics for clustering Bitcoin blockchain entities.
result Introduced clustering ratio to measure heuristic effectiveness.
We prove that curves indicated in the title exist. This results answers to a question posed by A.G.Vitushkin about 30 years ago. We also discuss the minimal number of boundary components of a curve in the unit ball passing through the center, under the condition that all these components are shorter than a given number…
Minimal surfaces in hyperbolic space have area bounds.
problem Bounding the area of minimal surfaces in geodesic balls of hyperbolic space.
method Proving an area lower bound for minimal submanifolds in a geodesic ball of hyperbolic space.
result The area of minimal surfaces is no less than the area of totally geodesic surfaces.
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data. Our model can factorize any set of entities and relations that can be represented by a relational model, including tensors and also multiple relations for each entity. Macau can also incorporate side information, specificall…
Enhances machine learning for dynamic, interconnected entities.
problem Lack of systematic feature engineering for dynamic, interconnected entities.
method Augments current graph machine learning with comprehensive feature engineering in space and time.
result Improves supervised learning on heterogeneous, attributed entities interacting over time.
LinkNBed learns entity and relationship representations across multiple graphs.
problem Jointly learn over multiple graphs and construct a unified graph.
method LinkNBed is a deep relational learning framework that learns entity and relationship representations across multiple graphs. It identifies entity linkage as a vital component and designs a novel objective to leverage it.
result Substantial improvements in link prediction and entity linkage over state-of-the-art relational learning approaches.
Minimal surfaces in a ball have a minimum area bound.
problem Finding the minimum area of minimal surfaces passing through a point in a ball.
method Analyzing minimal submanifolds in the unit ball with a prescribed point and bounding their area.
result The area of minimal surfaces is bounded by $(1-|y|^2)^{ rac{k}{2}}$.
Paper proposes neural approach for Chinese named entity recognition.
problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datas…
Web service embeds Wikidata entities for multilingual access.
problem Lack of easy access to Wikidata entities in various languages.
method Trained Word2Vec embeddings on Wikidata dump, exposed via REST API.
result Multilingual resource for over 600,000 Wikidata items and properties.
New model predicts fine-grained types for high-multiplicity entities.
problem Fine-grained entity typing with high type multiplicity.
method Set-prediction approach to high-multiplicity fine-grained typing.
result Model outperforms baselines on Wikipedia-based corpus.
A system predicts future malicious behavior of network entities.
problem Prioritizing alert data and understanding attack recurrence.
method Machine learning-based network entity reputation database system.
result It is possible to precisely estimate future attack probabilities.
REFIL learns from imagined sub-group interactions to improve multi-agent reinforcement learning.
problem Learning efficiency in multi-agent reinforcement learning settings.
method Randomized Entity-wise Factorization (REFIL) approach.
result REFIL outperforms all strong baselines in challenging multi-task StarCraft settings.
Novel framework for efficient entity alignment labeling.
problem Efficient labeling of entity alignments in knowledge graphs.
method Active and passive learning strategies to select informative instances.
result Passive learning achieves comparable performance to active learning.
A CNN model learns complex relationships in knowledge graphs.
problem Exploring complex relationships between entities and relationships in knowledge graphs.
method A Convolutional Neural Network (CNN) is used to learn entity and relationship representations in knowledge graphs.
result The proposed model outperforms state-of-the-art models on exploring unseen relationships.
Entity resolution (ER) is the task of identifying records belonging to the same entity (e.g. individual, group) across one or multiple databases. Ironically, it has multiple names: deduplication and record linkage, among others. In this paper we survey metrics used to evaluate ER results in order to iteratively improve…
Unified model detects anomalies in heterogeneous categorical events.
problem Detecting anomalies in events with mixed types and large event space.
method APE model that embeds entities into a latent space and models event probability via pairwise interactions.
result Our model outperforms state-of-the-art techniques in detecting anomalies.
Improved NER in biomedical literature using weakly-supervised augmentation.
problem Challenging NER in scientific literature, especially for biomedical entities.
method Weakly-supervised data augmentation with a reference set.
result Iterative model retraining improves NER performance significantly.
The goal of this paper is to describe all local diffeomorphisms mapping a family of circles, in an open subset of $\r^3$, into straight lines. This paper contains two main results. The first is a complete description of the rectifiable collection of circles in $\r^3$ passing through one point. It turns out that to be r…
Paper proposes a method to predict EL difficulty using consensus-based labels.
problem Challenges in automatically identifying and resolving ambiguous entity mentions.
method Consensus-based method to generate difficulty labels, supervised classification with various features.
result EL difficulty can be accurately predicted with high accuracy.
Efficiently resolves entities via scaled Ewens--Pitman model.
problem Entity resolution in large datasets.
method Microclustering Ewens--Pitman model with variational inference.
result Significant speed-up in entity resolution with competitive performance.
We show that any objective risk measurement algorithm mandated by central banks for regulated financial entities will result in more risk being taken on by those financial entities than would otherwise be the case. Furthermore, the risks taken on by the regulated financial entities are far more systemically concentrate…
Paper proposes DCT for efficient hybrid parallel training of large recommendation models.
problem Training large recommendation models at scale with efficient communication.
method Dynamic Communication Thresholding (DCT) for both Data Parallelism and Model Parallelism.
result Reduces communication by 100x and 20x during DP and MP, respectively, improving training time by 37%.
Proposes a new method for predicting missing relations in knowledge graphs.
problem Predicting missing relations between entities in knowledge graphs.
method Relational message passing method considering only edge features without entity IDs.
result PathCon method outperforms state-of-the-art methods significantly.
HAKE embeds entities in polar coordinates to model semantic hierarchies in knowledge graphs.
problem Lack of modeling semantic hierarchies in knowledge graph embeddings.
method HAKE embeds entities in a polar coordinate system, where the radial coordinate represents hierarchy levels and the angular coordinate distinguishes entities at the same level.
result HAKE significantly outperforms existing methods on link prediction tasks in knowledge graphs.
Framework models graph-connected entities with sparse shared HMMs.
problem Model sequential data from graph-connected entities.
method Sparse Mixture of Hidden Markov Models (HMMs) trained jointly with graph topology.
result Effectiveness and versatility demonstrated in experiments.
MR-GNN predicts interactions between structured entities using multi-resolution and dual graph neural networks.
problem Predicting interactions between structured entities, especially considering features in substructures of different sizes and interactions between entities.
method MR-GNN uses a multi-resolution architecture and dual graph-state L-STMs to extract features from different neighborhoods and pairwise graphs, respectively.
result MR-GNN improves prediction accuracy compared to state-of-the-art methods.
The paper tackles uniform sampling from databases with duplicates.
problem Sampling uniformly from entities with duplicate records.
method Two-stage process: frequency estimation followed by rejection sampling.
result Efficient sampling algorithms under various data properties.
Greek POS Tagger and Entity Recognizer built using spaCy.
problem Developing a machine learning model for Greek language POS tagging and named entity recognition.
method Machine learning approach with spaCy platform, focusing on morphological features and token classification.
result The Greek spaCy platform improved performance in part-of-speech tagging and named entity recognition.
Efficient autoregressive entity linking with correction for faster, more accurate results.
problem High computational cost and non-parallelizable decoding in autoregressive entity linking.
method Parallelizes autoregressive linking across all mentions, uses a shallow decoder, and adds a discriminative correction term.
result 70 times faster and more accurate than previous methods, outperforming state-of-the-art approaches.