Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Hybrid model combines machine learning and knowledge-based forecasting for chaotic systems.
problem Improving accuracy of chaotic system forecasts using limited knowledge.
method Combining machine learning with a knowledge-based model.
result Hybrid model predicts longer into the future than either component alone.
Paper proposes hybrid machine learning for tuning first principles models in engineering systems.
problem Inaccurate first principles models in process engineering due to changing conditions.
method Hybrid machine learning framework using Bayesian Neural Networks.
result Uncertainty estimates improve operation decisions in multiphase flow modeling.
Combining automata learning and model-based testing generates sufficient training data for hybrid systems.
problem Challenges in constructing models of hybrid systems that combine physical and digital behavior.
method Exploits a combination of automata learning and model-based testing to generate sufficient training data.
result Recurrent neural networks trained with this data achieved significantly better results compared to models learned from random data.
The paper introduces new tests for global controllability in hybrid systems.
problem Global controllability in hybrid systems with discrete events.
method Geometric formulation of hybrid systems and analysis of jump points.
result Hybrid systems can be globally controllable even if continuous systems are not.
Defines hybrid systems on principal bundles and studies impact effects.
problem Understanding impact effects in hybrid mechanical systems.
method Defines hybrid systems on principal bundles, studies underlying geometry, and finds conditions for impact preservation.
result Conditions for preservation of both exterior and interior impacts by mechanical connections.
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
NSC classifies hybrid system states for time-bounded reachability, achieving high accuracy with minimal false negatives.
problem Classifying states in hybrid systems for time-bounded reachability.
method Neural State Classification using Deep Neural Networks.
result Achieved 99.25% to 99.98% accuracy with false-negative rates reduced to 0.0015 to 0 after tuning.
Paper proposes a hybrid system to address bias in recommender systems.
problem Bias in recommender systems exacerbates existing societal inequalities.
method Hybrid approach combining multiple similarity measures, content, and demographic info.
result Our model provides more accurate and fairer recommendations.
Hybrid controller combines model-based and policy-based reinforcement learning.
problem Combining model-based and policy-based reinforcement learning for stability and robustness.
method Designs a hybrid controller that interpolates a model-based linear controller and a differentiable policy.
result Proven to maintain stability and universal approximation properties.
Hybrid AI system combines technical, sentiment analysis for adaptive equity trading.
problem Traditional trading strategies fail during high volatility and regime shifts.
method Combines trend-following, mean-reversion, sentiment analysis, machine learning, and market regime filtering.
result Hybrid model achieved 135.49% return on investment over 24 months.
Pandora hybridizes human and machine methods to explain AI system failures.
problem Understanding and explaining failures in complex AI systems.
method Hybrid human-machine methods and tools for summarizing system malfunction.
result Detailed performance views help in analysis and debugging of AI systems.
Hybrid model combines MD and ML for fluid systems, enhancing accuracy and speed.
problem Accurate and efficient prediction of multiscale fluidic systems.
method Combines molecular dynamics with on-the-fly machine learning for macroscopic inputs and outputs.
result Substantial enhancement in speed and accuracy compared to full MD simulations.
Tool interprets EEGs with high sensitivity and low false alarms.
problem Improving real-time diagnosis of EEGs for clinicians.
method Hybrid machine learning system combining HMM and deep learning.
result Delivers sensitivity above 90% with specificity below 5%.
Hybrid quantum neural networks predict continuous variables.
problem Predicting continuous variables using quantum computing.
method Quantum classical hybrid neural networks for continuous variable prediction.
result Quantum neural networks outperform classical methods in continuous variable prediction.
In this work we present a new approach on studying dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have research the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generaliz…
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
When the Poincaré map associated with a periodic orbit of a hybrid dynamical system has constant-rank iterates, we demonstrate the existence of a constant-dimensional invariant subsystem near the orbit which attracts all nearby trajectories in finite time. This result shows that the long-term behavior of a hybrid model…
New model enables AI to learn autonomously.
problem Enabling AI to acquire domain knowledge.
method Hybrid model combining ontology, knowledge graph, and Logic Neural Network.
result System can enrich and extend its knowledge.
Method improves simulation accuracy by mitigating distribution shift in hybrid systems.
problem Mitigating distribution shift in machine-learning augmented hybrid simulation.
method Tangent-space regularized estimator to control distribution shift.
result Marked improvements in simulation accuracy, especially for systems with high distribution shift.
A hybrid approach uses RNNs to recommend news articles based on context and session history.
problem Challenging news recommendation due to varying user interests and factors.
method Context-aware, hybrid, deep learning approach using RNNs with additional information types.
result Significantly higher recommendation accuracy and catalog coverage compared to other session-based algorithms.
Hybrid ASR systems can model graphemes effectively using chenones, outperforming traditional methods.
problem Traditional hybrid ASR systems struggle with English's poor grapheme-phoneme correspondence.
method Leveraging tied context-dependent graphemes (chenones) to model graphemes directly.
result Chenone-based systems significantly outperform senone baselines by 4.5% to 11.1% on English datasets.
Hybrid models combine domain knowledge and data-driven learning for Earth observation.
problem Challenges in modelling Earth observation data with either purely mechanistic or data-driven methods.
method Gaussian process convolution models, specifically latent force models (LFMs), integrating physical knowledge into multioutput GP models.
result Model automatically estimates soil moisture persistence and discovers latent forces related to precipitation.
New learning-based methods improve spectral efficiency in mmWave full-duplex systems.
problem Residual self-interference and high pathloss in mmWave full-duplex systems.
method Proposed two learning schemes (ELM-HBF and CNN-HBF) using ADMM and MM algorithms for SI cancellation and joint HBF optimization.
result Learning-based schemes achieve at least 22.1% higher spectral efficiency and faster online prediction and training times.
New method finds basins of attraction without needing system models.
problem Determining basins of attraction (BoA) for nonlinear systems without prior knowledge.
method Hybrid Active Learning (HAL) method combining AST, AL, and DBS.
result Efficiently finds and labels boundary of BoA without model knowledge.
Hybrid approach combines user feedback and machine learning for predicting user satisfaction.
problem Measuring user satisfaction in large-scale conversational agent systems.
method Fusion of explicit user feedback and predictions from two machine-learned models trained on different data types.
result Hybrid approach significantly improves user satisfaction predictions.
Hybrid quantum-classical RL model solves standard benchmark tasks and proves quantum advantage.
problem Challenges in reinforcement learning, especially in solving standard benchmarking tasks.
method Parametrized quantum circuits in a hybrid quantum-classical RL model.
result Demonstrates quantum advantage in solving standard benchmarking tasks and intractable classical problems.
Hybrid system uses SVM, ANFIS, and expert knowledge for oil saturation prediction.
problem Predicting oil saturation from well logs in a noisy dataset.
method Two-stage DKFIS: SVM for classification, ANFIS for prediction, expert knowledge refinement.
result DKFIS improves prediction accuracy compared to ANFIS alone.
Deep learning improves recommendation systems for marketplaces.
problem Challenges in matrix factorization for marketplaces.
method Hybrid recommender system combining user-generated contents and user behavior data.
result Five lessons learned from deep learning experiments.
Hybrid model improves weather forecasting accuracy.
problem Combining physics and machine learning for better weather predictions.
method Offline and online training of a neural network to correct model errors in the IFS.
result The hybrid model reduces forecast errors and improves accuracy.
Enhances RSCNs with hybrid regularization for nonlinear dynamics.
problem Modeling nonlinear dynamic systems with uncertainties.
method Recurrent stochastic configuration networks with hybrid regularization.
result The method outperforms other models in nonlinear system identification and industrial tasks.
Researchers prove inequalities for reaction-diffusion systems using a new curvature-dimension condition.
problem Proving Li-Yau and Harnack inequalities for systems of linear reaction-diffusion equations.
method Introducing a hybrid curvature-dimension condition and proving a differential Harnack estimate.
result A Harnack inequality holds under the hybrid curvature-dimension condition CDhyb(0,d) with d<∞. A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilber…
Hybrid models combine interpretable and complex models for better performance and control.
problem Improving model performance and user transparency in machine learning.
method Investigates hybrid models from theory, taxonomy, and methodological perspectives.
result Hybrid models can outperform standalone black boxes and provide precise control over transparency.
Paper proposes machine learning models for more accurate road inspection.
problem Traditional road inspection systems have safety, energy, and cost issues.
method Hybrid machine learning models using surface deflection data from FWD tests.
result CMIS model outperforms other models with APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
A hybrid ASR system using conformer architecture improves word-error-rate and training speed.
problem Improving word-error-rate and training efficiency for hybrid ASR systems.
method Used conformer architecture, applied time downsampling, and transposed convolutions.
result Conformer-based hybrid model achieves competitive results and significantly outperforms BLSTM-based hybrid model.
A new autoencoder combines deep learning with SVD to reduce model complexity.
problem Overcoming the Kolmogorov barrier in high-dimensional systems.
method Learnable weighted hybrid autoencoder combining SVD and deep learning.
result Empirically, the model exhibits a sharpness thousands of times smaller than other models.
Hybrid model predicts flow and pressure in water systems.
problem Predicting flow and pressure in water distribution systems with complex spatial-temporal correlations.
method Hybrid dual-stage spatial-temporal attention-based recurrent neural networks (hDS-RNN).
result Our model outperformed 9 baseline models in flow and pressure series prediction.
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
problem The evolution of stablecoins from crypto innovation to a global monetary component.
method Econometric analysis and hybrid system design modeling.
result Stablecoins maintain strong peg stability, and a hybrid system design ensures financial resilience.
A new system detects and classifies defects in semiconductor manufacturing.
problem Detecting and classifying novel defect patterns in high-resolution imagery.
method Stacked hybrid convolutional neural networks (SH-CNN) with visual attention.
result SH-CNN outperforms current approaches in automated visual inspection.
Paper develops a framework to identify latent dynamics from high-dimensional data.
problem Identifying latent dynamics from high-dimensional time-series data.
method Combines physics inductive bias and learn-to-identify strategy.
result Meta-HyLaD framework effectively identifies hybrid latent dynamics.
Hybrid LSTM-PPO optimizes dynamic portfolios with better performance.
problem Dynamic portfolio optimization under non-stationary market conditions.
method Combines LSTM for forecasting and PPO for adaptive portfolio adjustments.
result Hybrid framework outperforms single-model and equal-weight approaches in various metrics.
Model-based machine learning improves communication systems.
problem Improving symbol detection in communication receivers.
method Review and comparison of model-based and deep learning approaches, focusing on deep unfolding and DNN-aided hybrid algorithms.
result Different strategies of conventional deep architectures and hybrid algorithms show advantages and drawbacks.
New algorithms tackle multi-agent problems with hybrid action spaces.
problem Applying deep reinforcement learning to multi-agent problems with discrete-continuous hybrid action spaces.
method Proposed two novel algorithms: Deep MAPQN and Deep MAHHQN, using centralized training and decentralized execution.
result Empirical results show both algorithms significantly outperform existing methods.
The paper offers error bounds for quantized dynamical models.
problem Accuracy of dynamical models from dependent data sequences.
method Developed uniform error bounds for quantized models and imperfect optimization algorithms.
result Unified bounds for slow and fast rates, scaling with model encoding bits.
Unified framework for blending ML and mechanistic models in dynamical systems.
problem Learning dynamical systems from noisy, partially observed data.
method A unifying framework that combines mechanistic and machine learning approaches.
result Proves that hybrid models can learn memory-dependent model error.
This paper provides a geometrical derivation of the Hybrid Minimum Principle (HMP) for autonomous hybrid systems whose state manifolds constitute Lie groups (G,⋆) which are left invariant under the controlled dynamics of the system, and whose switching manifolds are defined as smooth embedded time invariant subma…
This work proposes robust reinforcement learning methods using both offline and online data.
problem Designing robust policies against parameter uncertainties in high-dimensional systems.
method Proposes RPQ for model-free learning with historical data and HyTQ for hybrid learning with both historical and online data.
result Unified analysis and theoretical guarantees for robust optimal policies in high-dimensional systems.