The paper explores the concept of predictive knowledge in reinforcement learning.
problem The relationship between predictions and knowledge in reinforcement learning is underdeveloped.
method The paper discusses the relationship between predictive knowledge learning methods and epistemic notions of justification and truth.
result The paper suggests the need for formalizing predictive knowledge in reinforcement learning.
Paper extends knowledge tracing algorithms to infer student knowledge and predict posttest performance.
problem Lack of algorithms that directly infer student knowledge and predict posttest performance.
method Extended DKT and DKVMN to infer knowledge, and applied to BKT and PFA for comparison.
result Knowledge estimates from the extended algorithms correlate better with posttest performance than existing methods.
The paper introduces a knowledge score for GPR predictions to assess their reliability.
problem Uncertainty in probabilistic predictions from Gaussian process regression models.
method A knowledge score quantifying the reduction of uncertainty in GPR predictions.
result The knowledge score improves prediction accuracy in various tasks.
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can …
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
New methods predict links in hypergraphs with multiple entities.
problem Link prediction in knowledge hypergraphs with non-binary relations.
method Introduce HSimplE and HypE embedding-based methods for hypergraphs.
result Proposed methods outperform baselines in hypergraph prediction.
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only availab…
New method regularizes deep networks by distilling self-knowledge.
problem Overfitting in deep neural networks.
method Self-knowledge distillation to regularize class-wise predictions.
result Significant improvement in generalization and calibration.
Tensor-based embeddings improve knowledge graph fact prediction.
problem Predicting new facts in knowledge graphs.
method Knowledge-Enriched Tensor Factorization
result 5% to 50% relative improvement over state-of-the-art techniques.
This work benchmarks neural embeddings for link prediction in evolving knowledge graphs.
problem Evaluating the robustness of neural embeddings in changing knowledge graphs.
method Proposes an open-source evaluation pipeline using relation-centric connectivity measures.
result Demonstrates the importance of simulating embedding accuracy for frequently updated knowledge graphs.
Refines neural network predictions using background knowledge for improved accuracy.
problem Compensate for lack of labeled data in neural networks.
method Introduces differentiable refinement functions and Iterative Local Refinement (ILR) algorithm to refine predictions efficiently and accurately.
result ILR finds competitive results in MNIST addition task and refines predictions on complex SAT formulas.
NAK model predicts student grades by focusing on relevant past courses.
problem Accurately predicting future course grades for students.
method NAK model learns attention weights to focus on relevant past courses.
result NAK model outperforms CKRM and other methods in predicting grades.
Proposes clustering as a new evaluation method for clinical knowledge embedding.
problem Traditional Link Prediction evaluation protocol loses information and harms model accuracy.
method Proposes Clustering Evaluation Protocol as an alternative.
result Experimental results show the proposed protocol can potentially replace Link Prediction.
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.
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
problem Insufficient performance of existing temporal models on extrapolated queries and time prediction.
method Introduced an extension to current temporal models using relative temporal information.
result Improved performance on extrapolated queries and time prediction.
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.
The paper improves neural network predictions by integrating process knowledge.
problem Improving neural network predictions for process execution data.
method Integrates background process knowledge into neural networks with attention mechanisms.
result Improves prediction accuracy for process execution data.
Proposes a method to integrate prior knowledge into trajectory prediction models.
problem Improving accuracy and robustness in trajectory prediction models.
method Continual learning approach that allows integration of arbitrary prior knowledge and probabilistic predictions.
result Outperforms non-informed and informed learning methods, using half as many observation examples.
Proposes a method to use causal graph knowledge for better predictive modeling.
problem Lack of effective ways to incorporate causal graph knowledge into predictive models.
method Model-agnostic data augmentation method exploiting CI relations encoded in causal graphs.
result Improves prediction accuracy, especially in small-data scenarios.
TuckER predicts missing facts in knowledge graphs using tensor decomposition.
problem Predicting missing facts in knowledge graphs.
method TuckER uses Tucker decomposition of binary tensor representations.
result TuckER outperforms state-of-the-art models in link prediction.
RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.
problem Interpreting GNN predictions for link prediction in heterogeneous settings is challenging.
method RAW-Explainer uses random walk objective and neural network to generate connected, concise subgraph explanations.
result RAW-Explainer strikes a balance between explanation quality and computational efficiency.
UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.
problem Lack of quantified predictive uncertainty in existing UnKGE methods.
method Proposes extsc{UnKGCP} framework using conformal prediction with a novel nonconformity measure.
result Sharp prediction intervals effectively capture predictive uncertainty in diverse UnKGE methods.
Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Tenso…
Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.
problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.
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.
The study categorizes knowledge graph relations and explains their embedding representations.
problem Understanding how knowledge graph relation representations capture semantic information.
method Categorizing knowledge graph relations into three types and deriving explicit requirements for their representations.
result Empirical properties of relation representations and the performance of methods are justified by the analysis.
Semantic TrueLearn uses semantic graphs to improve educational recommendation systems.
problem Challenges in handling semantic and hierarchical structure in knowledge areas.
method Introduces a novel learner model that exploits semantic relatedness between knowledge components using a Wikipedia link graph.
result Achieves statistically significant improvements in predictive performance for educational engagement.
Prediction in a small-sized sample with a large number of covariates, the "small n, large p" problem, is challenging. This setting is encountered in multiple applications, such as precision medicine, where obtaining additional samples can be extremely costly or even impossible, and extensive research effort has recentl…
Novel model improves clinical risk prediction by transferring knowledge between tasks over time.
problem Negative transfer in multi-task learning for clinical risk prediction.
method Temporal Probabilistic Asymmetric Multi-Task Learning (TPAMTL).
result Significantly outperforms various deep learning models for time-series prediction.
Domain knowledge helps detect adversarial examples in multi-label classification.
problem Detecting adversarial examples in multi-label classification.
method Convert domain knowledge into constraints and inject them into a semi-supervised learning problem.
result Domain-knowledge constraints help detect adversarial examples effectively.
RotatE embeds knowledge graphs using rotations in complex space for better link prediction.
problem Predicting missing links in knowledge graphs.
method RotatE models relations as rotations in complex vector space, using self-adversarial negative sampling.
result RotatE models and infers various relation patterns, outperforming existing models.
Proposes DPGN for integrating physics knowledge into graph networks for climate prediction.
problem Lack of explicit physics knowledge in deep neural networks.
method Integrates implicit physics knowledge from domain experts into latent space of DPGN.
result Significant improvement in climate prediction tasks.
DMLreg uses expert knowledge to improve model performance in high-dimensional settings.
problem Improving model performance in high-dimensional prediction problems.
method Learning a Mahalanobis distance metric from expert comparisons and integrating it into a regularized linear model.
result DMLreg leads to improvements in model performance when expert knowledge is relevant.
Proposes new models to predict student grades more accurately.
problem Accurately predicting future grades considering prior and concurrent courses.
method Context-aware, non-linear, and neural attentive models.
result Models outperform existing methods in predicting student grades.
RE-NET predicts future interactions in temporal knowledge graphs.
problem Predicting future facts in temporal knowledge graphs.
method Autoregressive architecture with recurrent event encoder and neighborhood aggregator.
result State-of-the-art performance on five public datasets.
New method predicts drug side effects from combined use.
problem Predicting side effects from drug combinations.
method Multi-relational knowledge graph completion.
result State-of-the-art results on polypharmacy side effect prediction.
A new tensor-based method for predicting temporal relationships in knowledge bases.
problem Predicting temporal relationships in evolving knowledge bases.
method Tensor decomposition of order 4 with new regularization schemes.
result Achieves state-of-the-art performance in temporal link prediction.
FAB-PPI uses prior knowledge to improve prediction-powered inference.
problem Improving statistical inference with machine learning predictions.
method Informing PPI with prior knowledge on prediction quality.
result FAB-PPI improves inference accuracy and confidence intervals.
FMs predict student knowledge accurately and efficiently, even with sparse data.
problem Predicting student knowledge progression over time.
method Factorization Machines (FMs) as a regression or classification model.
result FMs can accurately estimate student knowledge, even with sparse data.
Efficiently tunes hyperparameters for knowledge graph embeddings.
problem Hyperparameter tuning is challenging and costly for knowledge graph embeddings.
method Augmented probabilistic framework with variational expectation-maximization.
result Achieved state-of-the-art performance in link prediction.
BioNAS optimizes deep learning models for biomedical research, revealing new knowledge.
problem Building interpretable deep learning models for biomedical research.
method Neural architecture search with knowledge dissimilarity functions for joint optimization of predictive power and biological knowledge.
result BioNAS optimal models reveal novel knowledge in both simulated and real functional genomics data.
Link prediction in large knowledge graphs has received a lot of attention recently because of its importance for inferring missing relations and for completing and improving noisily extracted knowledge graphs. Over the years a number of machine learning researchers have presented various models for predicting the prese…
DRUM discovers interpretable rules from knowledge graphs for unseen entities.
problem Inductive link prediction on unseen entities and lack of interpretability.
method Differentiable approach using bidirectional RNNs for low-rank tensor approximation.
result DRUM outperforms existing methods in inductive link prediction.
NePTuNe combines neural and tensor methods for efficient link prediction in knowledge graphs.
problem Incomplete knowledge graphs, especially in link prediction.
method Hybrid model combining neural and tensor factorization methods.
result NePTuNe achieves state-of-the-art performance on FB15K-237 and near state-of-the-art on WN18RR datasets.
A new hypernetwork architecture improves link prediction in knowledge graphs.
problem Link prediction in incomplete knowledge graphs.
method Hypernetwork architecture generating simplified relation-specific convolutional filters.
result Hypernetwork outperforms ConvE and previous approaches across standard datasets.
This paper compares deep learning and knowledge-based methods for pedestrian trajectory prediction.
problem Predicting pedestrian trajectories in crowded scenes is challenging due to external factors.
method Comprehensive comparison of deep learning and knowledge-based models.
result Deep learning models outperform knowledge-based models in local trajectory prediction.
Enhances knowledge graph completion with mixed geometry tensor factorization.
problem Capturing nuanced distributional properties in knowledge graphs.
method Combines Euclidean and hyperbolic geometries for tensor factorization.
result Improves link prediction accuracy with fewer parameters.
Develops neural network for implied volatility surface prediction with financial domain knowledge.
problem Predicting implied volatility surface using neural networks.
method Incorporates prior financial domain knowledge into neural network architecture and training process.
result Model outperforms benchmarks and satisfies financial conditions.