Proposes a joint POI embedding model for better trip recommendations.
problem Trip recommendation needs to consider multiple contextual factors for better user satisfaction.
method Jointly learns the impact of POI popularity, co-occurring POIs, and user preferences.
result Proposed algorithms outperform state-of-the-art in trip recommendation quality.
CADE learns dual node representations for better generalization.
problem Transductive graph embeddings cannot generalize to unseen nodes or across different graphs.
method CADE combines real-time neighborhoods with neighbor-attentioned representation, preserving known node memory.
result CADE outperforms state-of-the-art methods in generalization and context-awareness.
Integrates contextual constraints into embedding models for better recommendation quality.
problem Contextual constraints lead to incomplete or low-quality recommendations when applied independently.
method Merges constraint application and retrieval into one operation in the embedding space.
result Significant improvements in predictive performance compared to context-aware and standard models.
Improved Capsule Networks for better image restoration and context awareness.
problem Challenges in image context recognition for Capsule Networks.
method Expanding Pooling layers and increasing Reconstruction layers; comparing CNs with DL models on ASL dataset.
result Capsule Networks perform comparably to DL models but significantly reduce training time.
Next basket recommendation improved with context-aware item representations.
problem Predicting users' next purchases based on historical transactions.
method Pre-trained context-aware item representations using transformers.
result IERT outperforms state-of-the-art methods in next basket prediction.
CaGAT learns context-aware edge representations for graph data.
problem Ignoring edge representation in GNNs.
method Unified Context-aware Adaptive Graph Attention Network (CaGAT) that learns both node and edge representations.
result CaGAT improves performance on semi-supervised learning tasks.
A new model for context-aware recommendations using LSTM and latent context.
problem Challenges in incorporating context into recommendation models, especially sparsity and dimensionality issues.
method Sequential latent context modeling using LSTM, reducing context dimensions to a compressed latent space.
result The proposed SLCM outperforms state-of-the-art CARS models in empirical analysis.
This paper discusses issues in mining user behavioral rules for context-aware mobile apps.
problem Mining contextual behavioral rules from smartphone data.
method Addressing quality of data, relevancy of contexts, discretization, rule discovery, semantic understanding, and dynamic rule updating.
result Potential solutions for mining user behavioral rules for context-aware mobile apps.
CAZSL learns to generalize physical interactions from context.
problem Designing models that can generalize over unknown objects during manipulation.
method Context-aware zero-shot learning using Siamese network architecture, embedding space masking, and regularization.
result CAZSL models can generalize to different parameters or features of interacting objects.
We generate transformation-invariant CNNs using context-aware filters.
problem Creating transformation-invariant neural networks for image recognition.
method Input-conditioned convolution filters combined with max-pooling and multi-instance learning.
result Significantly improved error rates on MNIST variations (1.13% on MNIST-rot-12k, 1.12% on Half-rotated MNIST, 0.68% on Scaling MNIST).
Proposes BehavDT model for context-aware user behavior prediction.
problem Building a context-aware predictive model based on diverse user behavioral activities.
method Introduces BehavDT, a behavioral decision tree that considers user behavior-oriented generalization.
result BehavDT model outperforms traditional machine learning approaches in predicting user diverse behaviors considering multi-dimensional contexts.
Proposes E-MIIM for better mobile phone interruptions management.
problem Improper phone interruptions in daily life activities.
method Ensemble learning based on context-aware mobile telephony model.
result E-MIIM outperforms existing MIIM models in prediction accuracy.
Context-aware ZSL improves object recognition by considering object context.
problem Previous ZSL approaches ignore object context, limiting their effectiveness.
method Proposes a new approach that models the conditional likelihood of objects appearing in specific contexts.
result Contextual information significantly improves ZSL performance and is robust to class imbalance.
Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.
problem Context-dependent activity recognition and unknown contexts in wearable computing.
method Developed the α-{eta} network coupled with uncertainty quantification (UQ) based on maximum entropy.
result Improved accuracy and F-score by 10% through high-level context identification.
Context-aware CNN improves cancer grading accuracy.
problem Grading colorectal cancer histology images accurately.
method Proposes a context-aware neural network for 1,792x1,792 pixel images.
result Outperforms traditional methods by 3.61%.
AppsPred predicts smartphone app usage based on context.
problem Predicting personalized usage behavior of smartphone apps based on contexts.
method Random Forest machine learning technique considering multi-dimensional contexts.
result AppsPred significantly outperforms other machine learning approaches in predicting smartphone apps.
We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects. CAB can be easily deployed in a real-world recommendation system, where multi-armed bandits have been shown to perform well in particular with respect to the cold-start problem. CAB utilizes a context-a…
A real-time context-aware system for IoT using mobile devices.
problem Challenges in running machine learning on mobile devices.
method Developed a context-learning algorithm for mobile devices that updates itself periodically from the server.
result Achieved mean accuracy of 97.51% with only 11ms execution time.
This paper improves context-aware recommender systems by selecting and incorporating relevant low-dimensional contextual information.
problem Generating accurate recommendations is not enough; contextual information can cause issues like battery drain and privacy.
method Developed a feature-selection algorithm based on genetic algorithms to reduce context dimensions while maintaining explainability.
result The approach improves accuracy and transparency in recommendations, outperforming state-of-the-art models.
Proposes a context-aware approach to deep autoencoder novelty detection.
problem Challenges of static distribution in novelty detection.
method Semi-supervised network architecture with auxiliary labels for contextual information.
result Single model achieves performance of individually trained models on various contexts.
This work improves local differential privacy by considering context to make it more effective.
problem Local differential privacy often sacrifices utility, especially for sensitive data.
method Introduces context-aware local differential privacy, optimizing privacy and utility.
result Contextual information can reduce the number of samples needed for privacy compared to classical LDP.
Faster, context-aware topic modeling approach unveiled.
problem Slow and heavy preprocessing in topic modeling.
method Conceptualizes topics as independent axes, decomposes embeddings using ICA.
result 4.5x faster than BERTopic on average, with diverse and coherent topics.
This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maxi…
MTL2L learns to adapt optimisation rules for unseen data.
problem Learners need to adapt to unseen data domains.
method Introduces MTL2L, a context-aware neural optimiser.
result MTL2L can adapt optimisation rules for unseen data.
LangDA improves domain adaptation for semantic segmentation by learning context-aware scene descriptions.
problem Improving domain adaptation for semantic segmentation with dense prediction tasks.
method LangDA learns contextual relationships between objects via VLM-generated scene descriptions and aligns image features with text representation.
result LangDA sets new state-of-the-art across three DASS benchmarks, outperforming existing methods.
CSA improves recommender systems by learning context-aware feature representations.
problem Limited expressiveness of traditional IMC methods for feature representations.
method Generalizes self-attention mechanism to IMC, learning context-aware feature representations.
result Extensive experiments show CSA's effectiveness on real RS datasets.
New algorithms improve semantic similarity in mobile datasets by incorporating context.
problem Traditional models fail to capture external context in mobile datasets.
method Developed new algorithms to integrate external features into sentence embeddings and semantic similarity scores.
result Applying PCA with eight components and appending multimodal features improves semantic understanding in tweets.
Improved LID for multilingual speakers using context-aware models.
problem Low accuracy for languages spoken by multilingual speakers, especially with accented speech.
method Coarser-grained acoustic model and integration with interaction context signals.
result Average 97% accuracy across all language combinations, 60% improvement in worst-case accuracy.
Proposes a model to understand urban dynamics from mega-metropolises.
problem Understanding residents mobility patterns in mega-metropolises.
method Neighbor-Regularized and context-aware Non-negative Tensor Factorization (NR-cNTF).
result NR-cNTF accurately captures city rhythms and spatial communities.
Enhances currency strategy Sharpe ratio by 30% using context-aware Learning to Rank.
problem Sub-optimal ranking of assets during critical market periods.
method Context-aware Learning to Rank model based on Transformer architecture.
result Significantly improves Sharpe ratio and various performance metrics.
ABC learns context-aware representations for clustering.
problem Learning latent representations that adapt to context in machine learning.
method Attention-based neural architecture that learns a similarity kernel.
result Competitive results for clustering Omniglot characters.
Paper proposes an active multi-step TD algorithm for reinforcement learning.
problem Challenging decision making and control tasks in reinforcement learning.
method Active stepsize learning and adaptive multi-step TD algorithm with context-aware mechanism.
result Competitive results compared to other reinforcement learning baselines on discrete and continuous space tasks.
MacroHFT uses memory and context-aware reinforcement learning to improve HFT performance.
problem Overfitting and biased decisions in HFT due to rapid market changes.
method Memory Augmented Context-aware Reinforcement Learning (MacroHFT) that trains multiple sub-agents and a hyper-agent.
result MacroHFT achieves state-of-the-art performance on minute-level trading tasks.
GD networks improve lung nodule classification accuracy.
problem Difficult classification of lung nodules of varying sizes.
method Proposes Gated-Dilated (GD) networks with Context-Aware sub-network.
result GD network outperforms state-of-the-art models with AUC > 0.95.
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.
GPLVMF improves CARS performance by addressing overfitting and context importance.
problem Overfitting and lack of automatic context importance determination in GP-based CARS.
method GPLVMF applies a non-zero mean function and real-valued latent space to improve GP model performance.
result Significant improvement in performance on real datasets and automatic context importance determination.
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
Proposes a new model for context-dependent decision-making.
problem Constant preference parameters in decision models are too rigid.
method Introduces Context-aware Bayesian mixed multinomial logit model using neural networks.
result Models context-dependent intra-respondent heterogeneity effectively.
Researchers create a framework to value player actions in CSGO.
problem Lack of accessible data and analytical frameworks for esports players.
method Data model, graph distance measure, context-aware framework.
result Demonstrated framework's consistency and independence compared to existing methods.
Particles representing tokens cluster in Transformers, influenced by initial tokens and matrix spectrum.
problem Understanding the geometry of learned representations in Transformers.
method Viewing Transformers as particle systems, applying dynamical systems and partial differential equations.
result Particles cluster towards limiting objects, confirming context-awareness and the emergence of leaders.
We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the dev…
MISIM improves code similarity systems with neural learning.
problem Improving accuracy of code semantics similarity systems.
method Context-aware semantics structure and neural code similarity scoring algorithm.
result MISIM achieves 8.08% better accuracy than state-of-the-art systems.
MQTransformer improves forecast accuracy with context-aware attention.
problem Improving probabilistic demand prediction accuracy.
method Incorporates Transformer architectures for context alignment and feedback-aware attention.
result Significant improvements in forecast accuracy, reducing excess variability.
Enhances sequence labeling with embedded-state latent CRFs.
problem Complex non-local constraints between sequence labels.
method Integrates multiple hidden states with low-rank log-potential scoring matrices.
result Model outperforms baseline CRF+RNN models with global constraints.
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
A new system recommends educational content based on student and peer interests.
problem Recommending suitable educational content for students.
method Neural Educational Recommendation Engine (NERE) using a recurrent neural network.
result NERE achieves higher recall and R^2 scores than traditional methods.
Unified view of context in machine learning models.
problem Exploring context in machine learning models.
method Decompose observation into context-free and context-sensitive parts.
result Significant performance boost in sentence embedding models.
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
problem Limited real calibration data for online anomaly detection.
method Context-aware prediction-powered conformal online anomaly detection (C-PP-COAD).
result Significantly reduces dependency on real calibration data without compromising FDR control.