Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.
problem One-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario.
method Formulated off-grid DOA estimation model, used Sparse Bayesian framework, proposed Learning-based Sparse Bayesian approach.
result Improved computational efficiency and accuracy in off-grid DOA estimation.
A neural network learns to estimate spectra from few noisy samples.
problem Estimating spectra from limited noisy data.
method Training a neural network on simulated data to approximate multisinusoidal signal spectra.
result The approach performs well in various noise conditions and is competitive with classical methods.
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
Deep learning predicts vehicle behavior for safer autonomous driving.
problem Enhance autonomous vehicles' hazard awareness in complex environments.
method Review of deep learning-based approaches for vehicle behaviour prediction.
result Deep learning outperforms conventional methods in complex scenarios.
Learning-based link scheduling improves network performance in millimeter-wave multi-connectivity.
problem Efficient link scheduling is crucial for maximizing network performance in millimeter-wave multi-connectivity.
method A learning-based approach to predict optimal link scheduling.
result The learning-based solution outperforms base line methods and approaches the optimal solution.
Deep learning solves non-Markovian FBSDEs for utility maximization.
problem Solving utility maximization problems under rough volatility.
method Deep learning-based numerical methods for non-Markovian fully coupled FBSDEs.
result Error estimates and convergence provided for the deep learning approach.
New algorithm for RL using mean embeddings of return distributions.
problem Improving reinforcement learning algorithms for dynamic programming.
method Mean embeddings of return distributions, novel algorithms for RL.
result Asymptotic convergence and improved performance in deep RL.
A learning-based approach optimizes automated lane changes with mixed-integer optimization and machine learning.
problem Traditional motion planning methods are inefficient and lack generalization capability.
method Mixed-Integer Quadratic Problem (MIQP) for optimal trajectories, supervised learning for fast decision-making.
result The proposed model outperforms existing motion planning methods in optimality, efficiency, and generalization.
Deep learning improves gender classification from handwriting.
problem Classifying gender from handwritten text.
method Convolutional Neural Network (CNN) for feature extraction and gender classification.
result Deep learning approach outperforms human examiners in gender classification accuracy.
Reinforcement learning optimizes neural network execution costs.
problem Minimizing execution cost of neural network computation graphs.
method Offline training of a reinforcement learning optimizer for generalization to unseen graphs.
result Significant improvements in execution time and memory usage compared to baselines.
Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective fun…
Transfer learning boosts deep learning in source code modeling.
problem Deep learning models are problem-specific and data-hungry.
method Transfer learning to improve performance of deep learning models.
result The proposed approach outperforms state-of-the-art models in source code suggestion task.
New deep learning method solves TSP faster and more efficiently.
problem Approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.
method Uses Graph Convolutional Networks for efficient TSP graph representations and non-autoregressive beam search.
result Significantly reduces optimality gap for large problem instances.
Deep learning tackles X-ray noise without clean data.
problem Lack of clean X-ray images for deep learning denoising.
method Uses Stein's Unbiased Risk Estimator (SURE) to train a deep neural network.
result SURE-based approach effectively denoises X-ray images.
New method combines QQA and gradient-based sampling for combinatorial optimization.
problem Scalability challenges in learning-based solvers for combinatorial optimization.
method Integrates gradient-based update through continuous relaxation with Quasi-Quantum Annealing (QQA) and parallel communication.
result Achieves superior speed-quality trade-offs for large-scale instances.
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
Paper introduces a new deep-learning method for quantum mechanics.
problem Simulating time-evolving Schrödinger equations efficiently.
method Generative diffusion models and stochastic mechanics.
result Significantly lower computational complexity compared to existing methods.
Study of learning-based inverse problems using GANs.
problem Solving inverse problems with learned priors.
method Developed a simple non-convex algorithm for certain GAN architectures.
result The approach achieves linear convergence guarantees and improves upon conventional techniques.
Reduces false positives in classifying rare online platforms.
problem Challenges in accurately identifying rare online platforms with ML.
method Calibrated probabilities and ensembles to reduce bias.
result Significantly reduces false positives in rare event detection.
Paper uses deep learning to solve PDEs without supervision.
problem Solving elliptic PDEs without labeled data.
method Uses deep neural networks and least-squares functionals.
result Demonstrates effectiveness on 1D second-order elliptic PDEs.
Study on robustness of learning-based novelty detection methods under adversarial attacks.
problem Understanding how learning-based novelty detection methods perform under adversarial perturbations.
method Formulated an oracle attack setup and evaluated vulnerability using black-box adversarial algorithms.
result Adversarial perturbations can significantly increase FDR while maintaining high detection power.
We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algor…
New rigorous uncertainty bounds for Gaussian Process regression.
problem Need for frequentist uncertainty bounds in applications like learning-based control.
method Introduce new uncertainty bounds that are rigorous and practically useful.
result New bounds are less conservative and more useful for practical applications.
CPAS uses machine learning to plan hospital resources for COVID-19.
problem Forecasting hospital resource demands during the COVID-19 pandemic.
method Combining machine learning algorithms with diverse data sources.
result CPAS successfully managed hospital resource planning in the UK.
Deep learning speeds up MRI image reconstruction from sparse data.
problem Efficiently reconstructing MRI images from limited data.
method Data-driven, model-driven, and integrated deep learning approaches.
result Potential for deep learning to significantly speed up MRI reconstruction.
Deep learning model approximates stochastic responses.
problem Approximating stochastic responses using neural networks.
method Generative neural network with conditional maximum mean discrepancy (CMMD) loss.
result Excellent performance on benchmark problems.
Paper tackles anomaly detection in driving data, improving AI system performance.
problem Detecting rare anomalous driving situations in safety-critical AI systems.
method Semi-supervised multi-task learning using maneuver labels for imbalanced data.
result Improved anomaly detection performance on real-world driving data.
Automates deep learning model development for cancer data.
problem Manual design of high-performing deep learning models for cancer data is time-consuming and requires expertise.
method Reinforcement-learning-based neural architecture search with custom building blocks.
result Automated discovery of deep neural network architectures with similar or higher accuracy.
It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \ci…
RankML predicts machine learning pipeline performance quickly and accurately.
problem Lack of machine learning experts and high computational costs.
method Meta-learning approach to predict pipeline performance.
result RankML outperforms or matches state-of-the-art approaches with less time and resources.
This study compares three portfolio optimization methods on Indian stocks.
problem Comparing portfolio optimization methods on Indian stocks.
method Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning approaches.
result Reinforcement Learning outperformed other methods in terms of Sharpe ratio.
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the i…
A meta-learning approach improves the performance of alternating minimization for non-convex optimization problems.
problem Optimizing non-convex problems with multiple variables using alternating minimization.
method Meta-learning based alternating minimization (MLAM) to replace handcrafted updating rules.
result The proposed MLAM method outperforms traditional AM-based methods in various non-convex optimization problems.
Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, i…
Charities can increase donations by targeting optimal recipients.
problem Ineffective fundraising leads to lower resources for goods.
method Combines field experiment and causal machine-learning approach.
result Machine-learning-based optimal targeting increases donations significantly.
This work evaluates machine learning-based hotspot detectors on synthesized layout patterns.
problem Evaluating model robustness and generality of machine learning-based hotspot detectors.
method Developed an automatic layout generation tool to synthesize various layout patterns and tested machine learning-based detectors on these synthesized layouts.
result Machine learning-based detectors need continuous study for robustness and generality in DFM flows.
Deep learning methods improve subsurface flow modeling efficiency.
problem Efficiently modeling subsurface flow with uncertain parameters.
method Two categories of deep-learning based inverse modeling methods: surrogate-based and direct.
result Deep-learning methods significantly accelerate subsurface flow modeling.
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
problem Imbalanced binary classification tasks where minority class is underrepresented.
method DAMVI algorithm increases positive example weights and optimizes classifier weights using PAC-Bayesian C-Bound.
result DAMVI outperforms state-of-the-art models on various imbalanced datasets.
Proposes a generic prediction architecture for autonomous vehicles considering both rational and irrational driving behaviors.
problem Accurately predicting future behaviors of surrounding vehicles for safe autonomous vehicle planning.
method Combines learning-based and planning-based models to address rationalities in human behavior.
result Stable prediction performance under various unseen driving scenarios.
A new unsupervised learning method calibrates rough volatility models efficiently.
problem Efficient calibration of rough volatility models with minimal data.
method Unsupervised learning using BSDE representation and neural networks.
result The proposed scheme minimizes loss and approximates BSDE solution.
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.
A reinforcement learning method selects the best short-term load forecasting model.
problem Challenges in selecting the best short-term load forecasting model under varying conditions.
method A Q-learning agent learns the optimal policy of selecting the best forecasting model for the next time step.
result The Q-learning algorithm converges fast and improves forecasting accuracy by approximately 50%.
Deep learning approximates SPDE solutions from noise trajectories.
problem Approximating solutions to stochastic partial differential equations (SPDEs).
method Uses neural networks to approximate SPDE solutions based on noise realizations.
result Accurately estimates SPDE solutions and functionals like mean and variance.
Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.
problem Learning with label proportions (LLP) for uncertain data.
method Transfer learning approach to transfer knowledge from source to target tasks with uncertain data.
result The proposed TL-LLP method achieves better accuracy and is less sensitive to noise.
Deep learning predicts crop prices with improved accuracy.
problem Accurate prediction of agricultural crop prices for better decision-making.
method Innovative deep learning approach using GNNs and CNN models.
result At least 20% better performance than previous literature.
New framework for task-independent legged locomotion.
problem Building stable legged locomotion systems in robotics.
method Task-independent spiking central pattern generator using learning methods.
result Robotic legged locomotion at different speeds and within the same gait cycle.
Study evaluates adversarial training for deep learning IDSs against various attacks.
problem Evasion attacks against deep learning-based IDSs.
method Investigated adversarial training using min-max approach on CNN and RNN.
result Adversarial training improves robustness against five attack methods.
A deep learning approach for efficient power control in wireless video transmissions.
problem Optimizing power control for real-time wireless video transmissions with quality constraints.
method Proposes a learning-based approach using a deep neural network to solve the non-convex power control problem.
result The deep neural network can quickly provide optimal power levels for given channel conditions.