In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which capture the relations among inputs. In many cases, more than two maps are strongly…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,982 papers · 148 categories
Trend · papers per month
48 results for “Relational learning”
Method learns relational features for Gaifman models from knowledge bases.
problem Structure learning for Gaifman models.
method Relational tree distances to learn relational features.
result Empirical evaluation shows superiority over classical rule-learning.
A new model improves relation extraction accuracy through relation-gated adversarial learning.
problem Relation extraction from sentences is challenging due to expensive human annotation and noisy distant supervision.
method Proposes relation-gated adversarial learning for relation extraction, extending domain adaptation methods.
result The model outperforms previous domain adaptation methods and improves accuracy of distance supervised relation extraction.
Bayesian meta-learning on relation graphs improves few-shot relation extraction.
problem Predicting relations in sentences with limited labeled examples.
method Bayesian meta-learning on a global relation graph, using graph neural networks and Langevin dynamics.
result Framework effectively learns and generalizes to new relations.
Bayesian weight priors improve neural network learning of identity relations.
problem Neural networks struggle to learn abstract and systematic relations, especially identity relations.
method Extended RBP approach using Bayesian weight priors as a regularization term.
result Bayesian weight priors lead to perfect generalization for identity relations and do not hinder standard neural network learning.
Method discovers nonlinear relations from time series data.
problem Identifying directional relations from nonlinear interactions in time series.
method Minimum predictive information regularization method for deep learning.
result Substantially outperforms other methods for learning nonlinear relations.
Relational Mimic improves visual imitation learning from video demonstrations.
problem Improving robustness and sample efficiency in visual imitation learning.
method Combines generative adversarial networks and relational learning.
result Improves agent performance in challenging locomotion tasks.
Paper introduces a new framework combining deep learning and logic for relational data.
problem Scalability and flexibility of deep learning methods for relational data.
method Combines auto-encoding principle with first-order logic and logic programs.
result Latent representations are more accurate, flexible, and interpretable.
MetaR learns few-shot link prediction in KGs by transferring relation-specific meta info.
problem Few-shot link prediction in KGs with limited associative triples.
method MetaR framework focusing on transferring relation-specific meta information.
result MetaR achieves state-of-the-art results on few-shot link prediction benchmarks.
Graph Neural Networks improve machine learning on relational databases.
problem Training machine learning models on relational databases requires costly data extraction and feature engineering.
method Uses Graph Neural Networks to extract features from relational databases.
result Outperforms state-of-the-art automatic feature engineering methods.
Python library for boosting statistical relational models.
problem Expressing learning and inference problems in statistical relational models.
method Adapting scikit-learn interface for boosted statistical relational models.
result Provides examples for using srlearn.
Develops a fair relational model learning algorithm.
problem Fairness in machine learning models for relational data.
method Fair-A3SL, a fairness-aware structure learning algorithm for relational structures.
result Demonstrates effectiveness in learning fair, interpretable, and expressive structures.
RelEx explains relational models without gradient access.
problem Lack of explainability for relational models like GNNs and SRL.
method Model-agnostic explainer for relational models using only outputs.
result Comparable or better performance compared to GNN-Explainer.
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalabili…
TGG improves zero-shot and few-shot learning by explicitly modeling and utilizing seen-unseen domain relations.
problem Lack of data in unseen domains hinders generalization in zero-shot and few-shot learning.
method TGG generates explicit instance-level graphs to model and utilize seen-unseen domain relations, addressing domain shift.
result TGG outperforms existing methods in zero-shot, generalized zero-shot, and few-shot learning.
Defines related tasks for transfer learning using foliations.
problem Lack of a foundational description of related tasks in transfer learning.
method Introduces foliations as a mathematical framework for relatedness between tasks.
result Identifies foliations as a way to represent relatedness in transfer learning.
Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recen…
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus on vectorized data and offer little support for relational data, which additionally describe relationships among instances. In this work we…
Proposes a new method to learn data representations by modeling sample relations.
problem Lack of rich latent structural information in DAEs.
method Explicitly models and leverages sample relations as supervision for representation learning.
result Significantly improves clustering performance on benchmark datasets.
HARMLESS meta-learning method models short event sequences with relational information.
problem Learning heterogeneous point process models from short event sequence data.
method Hierarchical Bayesian mixture Hawkes process model with stochastic variational meta expectation maximization.
result HARMLESS outperforms existing methods in predicting future events.
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches…
R-SQAIR adds relational bias to sequential object attention models for better object interactions.
problem Traditional sequential multi-object attention models struggle with relational inferences.
method Proposes R-SQAIR, a relational extension of SQAIR with a parallel pairwise interaction module.
result Demonstrates gains in object relations and combinatorial generalization over sequential mechanisms.
Introduces Relational Privacy (RP) to control relation memorization in question answering models.
problem Relation memorization in question answering models can lead to privacy issues.
method Formalizes Relational Privacy (RP) and Differential Relational Privacy (DrP), providing bounds on relation memorization.
result DrP allows effective learning of general properties of underlying concepts while preventing relation memorization.
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounderscs.AI
Algorithm learns causal structures from time-series data, reducing tests for temporal vs. contemporaneous relations.
problem Learning causal structures from time-series data with latent confounders.
method Constraint-based algorithm that refines a causal graph by learning temporal relations first, then contemporaneous ones.
result Reduces the number of statistical tests and improves accuracy for synthetic and real-world data.
Basic binary relations such as equality and inequality are fundamental to relational data structures. Neural networks should learn such relations and generalise to new unseen data. We show in this study, however, that this generalisation fails with standard feed-forward networks on binary vectors. Even when trained wit…
Language helps RL agents learn complex relational and causal structures.
problem Learning relational and causal structure in complex environments.
method Training RL agents to predict language descriptions and explanations.
result Language aids agents in learning challenging relational and causal tasks.
A neural network learns relational representations from raw data.
problem Learning reusable representations from raw pixel data.
method Explicitly relational neural network architecture trained on visual relational tasks.
result The architecture outperforms baselines on unseen tasks.
Paper proposes a novel RRL framework that learns from images and incorporates expert knowledge.
problem Lack of effective methods to incorporate expert background knowledge and learn from non-relational data in RRL.
method Differentiable Inductive Logic Programming (ILP) for learning relational information from images and incorporating expert knowledge.
result Efficacy demonstrated on various environments and datasets, showing improved learning and generalization.
Most of metric learning approaches are dedicated to be applied on data described by feature vectors, with some notable exceptions such as times series, trees or graphs. The objective of this paper is to propose a metric learning algorithm that specifically considers relational data. The proposed approach can take benef…
With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with var…
New autoencoder framework learns structured latent priors.
problem Learning autoencoders with flexible priors.
method Relational regularization on latent prior, scalable algorithms.
result RAE outperforms existing autoencoders in image generation.
GMNN combines conditional random fields and graph neural networks for relational data.
problem Semi-supervised object classification in relational data.
method Combines conditional random fields and graph neural networks. Uses variational EM algorithm for training.
result GMNN achieves state-of-the-art results on object classification, link classification, and unsupervised node representation learning.
TGCN learns from multi-relational graphs, improving SSL performance.
problem Scalable semi-supervised learning from multi-relational data.
method Tensor-graph convolutional network with dynamic weights and graph-based regularizers.
result Significantly improved SSL performance over standard GCNs.
Proposes a new tensor factorization model for better link prediction in knowledge graphs.
problem Lack of information in treating missing and non-existing relations equally in tensor factorization models.
method Introduces a binary tensor factorization model with probit link to address the issue.
result Shows improved prediction accuracy and interpretability compared to existing models.
Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our a…
Robots learn object dynamics from visuals using GNNs and relational biases.
problem Challenging for robots to reason like humans about physical interactions.
method Graph Neural Networks (GNNs) with relational inductive bias.
result Auto-Predictor outperforms GN-based models and auto-encoder baseline.
Unified approach for learning state representations from streaming data.
problem Learning reusable state representations from high-dimensional, non-stationary data.
method Unified mathematical formulation for learning latent relations, enabling flexible and principled shaping of latent space.
result Improved understanding and evaluation of existing unsupervised learning approaches.
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffe…
DGRCL integrates dynamic and static graph relations for financial market prediction.
problem Capturing the evolving nature of stock markets while considering both temporal changes and static relational structures.
method Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, including Embedding Enhancement (EE) and Contrastive Constrained Training (CCT) modules.
result DGRCL significantly outperforms state-of-the-art TGL baselines on NASDAQ and NYSE datasets.
ARML learns task relations to improve meta-learning efficiency.
problem Handling task heterogeneity in meta-learning.
method Automatically extracts cross-task relations and constructs a meta-knowledge graph.
result ARML outperforms state-of-the-art baselines in few-shot learning tasks.
Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational cha…
Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a …
This paper explores fairness in machine learning systems and its relation to democracy.
problem Discriminatory practices in machine learning systems, especially in criminal justice, credit scoring, and advertising.
method Analysis of fairness definitions in machine learning.
result Various definitions of fairness are closely related to different ideas of justice and democracy.
This paper characterizes projective models in statistical relational learning.
problem Projectivity in statistical relational models is beneficial for inference and learning.
method Representation theorems for infinite exchangeable arrays to characterize projective models.
result A class of directed graphical latent variable models correspond to projective relational models.
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is relate…
Bayesian approach improves network lasso for multi-task learning.
problem Improving the determination of relational coefficients in network lasso.
method Proposes a Bayesian approach to solve multi-task learning problems using network lasso.
result Objective determination of relational coefficients through Bayesian estimation.
Paper tackles zero-shot learning for semantic image interpretation.
problem Extracting structured semantic descriptions from images requires complete training sets, which are often unavailable.
method Uses Logic Tensor Networks to leverage logical constraints and similarities among relationships in the training set.
result Background knowledge can alleviate the incompleteness of training sets, improving zero-shot learning performance.