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.
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.
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.
Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules…
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…
We propose a novel visual context-aware filter generation module which incorporates contextual information present in images into Convolutional Neural Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set of learned convolution filters for all input image instances. Our proposed input-conditio…
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.
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.
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.
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.
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
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.
Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factoriz…
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.
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.
Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only intrinsic properties of objects, e.g. their visual appearance, are taken into account while their context, e.g. the surrounding objects in …
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.
Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand…
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.
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.
Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorize…
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.
Online learning algorithm identifies contextual changes for streaming data.
problem Challenges in identifying and utilizing contextual cues for streaming data.
method Neural network-based autoencoder for identifying contextual changes; comparison to a knowledge base of learned contexts.
result Classifier training benefits from automatically discovered contexts, demonstrating quicker learning convergence during contextual changes.
Novel meta-RL strategy improves efficiency in learning novel tasks.
problem Efficiency in learning novel tasks using deep RL.
method Decomposes meta-RL into task-exploration, task-inference, and task-fulfillment; uses deep networks and a task encoder.
result Improves sample efficiency and mitigates meta-overfitting.
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and cha…
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…
The paper develops a new framework for detecting distributional drifts conditioned on context.
problem Detecting distributional drifts in machine learning systems when context changes.
method Develops a framework using two-sample tests for conditional distributional treatment effects.
result Demonstrates effectiveness for detecting drift in subpopulations of data.
Next basket recommendation, which aims to predict the next a few items that a user most probably purchases given his historical transactions, plays a vital role in market basket analysis. From the viewpoint of item, an item could be purchased by different users together with different items, for different reasons. Ther…
Paper explores BERT's efficiency on SQuAD2.0, freezing layers and using adapters.
problem Improving BERT's efficiency for SQuAD2.0 while maintaining performance.
method Freezing transformer layers, using adapters, and context-aware convolutional filters.
result Context-aware convolutional filters do not improve practical efficiency.
Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. …
Bayes-CATSI uses variational Bayesian deep learning for medical time series data imputation.
problem Missing values in medical time series data.
method Bayes-CATSI integrates variational inference for uncertainty quantification and context-aware imputation.
result Bayes-CATSI outperforms CATSI by 9.57% in imputation performance.
CalBehav models individual smartphone user behavior for calendar events.
problem Static calendar models do not reflect individual user behavior.
method Machine learning, context-aware, personalized model using time-series smartphone data.
result Data-driven model more effective for managing incoming mobile communications.
MPPN network improves long-term time series forecasting accuracy.
problem Inaccurate long-term time series forecasting due to noise and lack of interpretability.
method MPPN network constructs context-aware multi-resolution semantic units and employs multi-periodic pattern mining and channel adaptive module.
result MPPN significantly outperforms state-of-the-art methods on nine real-world benchmarks.