Self-adaptive training improves deep learning robustness.
problem Improving deep learning performance on corrupted data.
method Dynamic correction of problematic labels using model predictions.
result Self-adaptive training significantly improves generalization over ERM under various levels of noise.
Proposes SAG-DBSCAN for clustering with self-adaptation.
problem Clustering analysis in data mining.
method Uses grey relational matrix and DBSCAN for clustering.
result Demonstrates superior performance compared to other methods.
M-L2O adapts fast to new tasks by self-adapting during test-time.
problem L2O optimizers struggle with out-of-distribution tasks.
method Meta-training an L2O optimizer to adapt quickly to new tasks.
result M-L2O converges significantly faster than vanilla L2O with only 5 steps of adaptation.
Paper proposes a new method for robust speaker verification.
problem Improving robustness in speaker verification systems.
method Combines soft VAD and self-adaptive VAD with DNN-based VAD.
result Significant improvement in verification performance in real-world environments.
A new clustering algorithm fuses heat diffusion and turning angle for robustness.
problem Cluster similar elements in various fields.
method Combines heat diffusion and maximal turning angle for robust fission clustering.
result The SARFC algorithm outperforms other methods in clustering performance.
Self-adaptive PINNs improve accuracy in stiff PDEs.
problem Accurate numerical solution of stiff PDEs using neural networks.
method Adaptive weights applied to each training point individually, using a soft attention mechanism.
result SA-PINNs outperform other PINN algorithms in L2 error with fewer training epochs.
Study on learning without i.i.d. assumptions, focusing on universally consistent function learning.
problem Develop a theory of learning without i.i.d. assumptions.
method Study universally consistent function learning under general stochastic processes, focusing on optimistically universal learning rules.
result Optimistically universal learning rules exist in the self-adaptive learning setting.
Improves neural networks by adjusting labels to reduce overfitting to noisy data.
problem Reduces overfitting to mislabeled or non-representative samples in neural networks.
method Combines self-adaptive training with mixup to improve accuracy and robustness.
result Achieves state-of-the-art accuracy on image recognition datasets with label noise.
Proposes EDESH-SA for better inventory management under uncertainty.
problem Inventory management under uncertainty.
method Ensemble Differential Evolution with simulation-based hybridization and self-adaptation.
result Improves financial performance and optimizes search spaces.
Holarchic structures improve decentralized deep learning performance.
problem Challenges in decentralized deep learning due to network uncertainties and resource limitations.
method Introduces self-adaptive holarchic learning structures to manage learning performance in distributed environments.
result Holarchic structures enhance learning performance in distributed environments with uncertainties.
A new method for optimizing using ensembles of kriging models.
problem Optimizing using traditional Gaussian Process (GP) methods is computationally expensive and may not be the most efficient approach.
method Proposes a novel approach where at each iteration, a small ensemble of kriging models with different length-scales is created and used to produce an iterate.
result The method shows potential for parallel implementations but does not perform better than classical EGO in a sequential context.
Paper introduces a novel error measure for neural networks integrating statistical and information theory.
problem No single error measure is universally best for neural network training.
method Developed a novel error measure EExpAbs and integrated it into the Levenberg-Marquardt algorithm. result Self-adaptive, dynamic learning algorithm improves both model accuracy and training process.
Paper presents a self-adaptive learning model for robust classification and regression.
problem Dealing with various datasets of different complexity.
method Combines DNDN and DSP, an end-to-end training approach with multiple randomly initialized softmax layers and adaptive soft pruning.
result The model demonstrates no performance loss compared with unpruned models and higher robustness over different data and feature distributions.
Paper proposes a neural network for learning crossmodal stimuli.
problem Improving crossmodal processing in dynamic environments.
method Deep neural architecture trained by expectation learning.
result Self-adaptable deep learning model for crossmodal stimuli.
AMI-Net+ tackles medical diagnosis from incomplete, imbalanced data.
problem Medical diagnosis from incomplete and imbalanced data.
method AMI-Net+ uses multi-instance neural network with embedding, multi-head attention, and gated attention-based pooling. It also employs focal loss and self-adaptive multi-instance pooling.
result AMI-Net+ outperforms state-of-the-art models on real-world medical datasets.
Speech enhancement improved by adapting to unknown speakers without auxiliary signals.
problem Improving speech enhancement accuracy for unknown speakers.
method Adopting multi-task learning for speech enhancement and speaker identification, using multi-head self-attention.
result Achieved state-of-the-art performance and improved subjective quality.
Enhances financial time series forecasting with a multi-period learning framework.
problem Accurate financial time series forecasting requires considering both short-term and long-term trends.
method Proposes a Multi-period Learning Framework (MLF) with three modules: Inter-period Redundancy Filtering, Learnable Weighted-average Integration, and Multi-period self-Adaptive Patching.
result Improves financial time series forecasting accuracy and efficiency.
Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting bi…
MASA framework uses RL to balance portfolio returns and risks.
problem Managing portfolio risk in turbulent financial markets.
method Multi-agent reinforcement learning with a market observer.
result MASA framework outperforms RL approaches in balancing returns and risks.
New bounds show agnostic multiclass learning depends on two dimensions: Natarajan and Daniely-Shalev-Shwartz.
problem Understanding sample complexity in multiclass classification with agnostic learning.
method Developed a novel online procedure based on a self-adaptive multiplicative-weights algorithm.
result Agnostic sample complexity bounds are in the form of DS^(1.5)/ε + Nat/ε^2, nearly tight up to a √DS factor.
Adaptive Bayesian method improves deep learning performance and robustness.
problem Ensuring sparsity in deep learning models to improve performance and robustness.
method Adaptive empirical Bayesian method with self-adaptive spike-and-slab priors and stochastic gradient MCMC optimization.
result The method achieves state-of-the-art performance on MNIST and Fashion MNIST and compression performance on CIFAR10.
New sensors enable activity recognition without user input.
problem Traditional activity recognition systems are limited by fixed sensor sets.
method Opportunistic activity recognition using unsupervised clustering and semi-supervised learning.
result Dynamic sensor input spaces lead to improved activity recognition.
FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
A new algorithm flattens multi-modal distributions for better deep learning.
problem Bayesian learning in big data with multi-modal distributions.
method Contour Stochastic Gradient Langevin Dynamics (CSGLD) algorithm.
result The CSGLD algorithm avoids local traps in deep neural networks.
VAV method optimizes learning rate for faster, stable SGD convergence.
problem Optimizing learning rate for efficient and stable machine learning models.
method Energy-based self-adaptive learning rate with auxiliary variable r. result VAV method achieves faster convergence and superior stability with larger learning rates.
SAPAG attacks distributed learning by reconstructing true training data from gradients.
problem Privacy attacks on distributed learning systems through gradients.
method SAPAG uses a Gaussian kernel-based gradient difference distance measure.
result SAPAG can reconstruct training data on various DNNs and at different training phases.
AAMDRL uses DRL to manage assets in noisy, changing environments.
problem Learning in noisy, self-adapting environments with sequential data.
method Augmented state information, one-period lag, walk forward analysis.
result AAMDRL outperforms traditional methods in asset management.
New algorithm reduces optimization complexity in adaptive mirror descent.
problem Optimizing complex, non-smooth, non-convex functions efficiently.
method SVRAMD: Variance Reduced Adaptive Mirror Descent.
result Variance reduction accelerates convergence in adaptive mirror descent.
Enhances physics-informed neural networks with adaptive sampling and weighting.
problem Challenges in training physics-informed neural networks on complex problems.
method Hybrid adaptive sampling and weighting method.
result Consistently improves prediction accuracy and training efficiency.
ICSGLD improves efficiency in posterior sampling for big data.
problem Efficient posterior sampling for large datasets.
method Embarrassingly parallel multiple-chain CSGLD with efficient interactions.
result ICSGLD is more efficient than a single-chain CSGLD.
Paper introduces timing-based adversarial attacks on DRL-based navigation systems.
problem Vulnerability of DRL-based navigation systems to adversarial attacks.
method Timing-based adversarial strategies using physical noise patterns.
result Adversarial timing attacks significantly degrade DRL-based navigation performance.
SAIL learns from sub-optimal demonstrations to improve sample efficiency in sparse reward tasks.
problem Reducing sample complexity in sparse-rewarded tasks.
method Self-Adaptive Imitation Learning (SAIL) that exploits sub-optimal demonstrations and efficient exploration.
result Significantly improved sample efficiency and better final performance across various tasks.
NoRML adapts RL policies without explicit rewards.
problem Adapting to new environments without external reward feedback.
method Extends MAML for RL using observable dynamics instead of reward.
result NoRML outperforms MAML in dynamic task adaptation.
Bayesian method selects sparse models efficiently with less bias.
problem Sparse model selection and regularization in Gaussian graphical models.
method Continuous spike-and-slab framework with EM algorithm for fast explorations.
result Efficient selection of sparse models with less bias compared to other methods.
Study improves self-driving safety in dynamic environments.
problem Safe self-driving in non-stationary urban settings.
method Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA).
result Self-driving agents can adapt safely in real-time.
Spectral clustering for directed graphs using likelihood estimation.
problem Clustering directed graphs with edge directions.
method Maximum likelihood estimation on stochastic block models.
result Significant performance gains over existing methods.
WamOL uses PINNs to efficiently calibrate IVS from sparse data.
problem Calibrating time-dependent IVS from sparse market data.
method Physics-Informed Neural Networks (PINNs) with adaptive reweighting.
result WamOL outperforms in calibrating intraday IVS from uneven data.
Proposes GIL for semi-supervised graph classification.
problem Semi-supervised classification of graph data with limited labeled nodes.
method Graph Inference Learning framework that learns node label inference from graph topology.
result Significantly improves semi-supervised node classification performance.
Automated medical protocol uses neural networks and decision trees.
problem Improving healthcare delivery through automated decision-making.
method Hybrid model combining neural networks and decision trees.
result Effective early decisions for patient care.
New method learns physical meanings in learned representations for better downstream tasks.
problem Lack of explicit physical meanings in learned representations from self-supervised learning.
method Analysis-by-synthesis approach with coordinated sampling and training.
result Trained models can control articulatory synthesizers to speak like humans and generalize well.
Adaptive weights improve physics-informed neural networks and deep operator networks.
problem Training physics-informed neural networks and deep operator networks can be challenging, leading to unsatisfactory accuracy and efficiency.
method Proposes a pointwise adaptive weighting method that balances the residual decay rate across different training points.
result Our proposed approach of balanced residual decay rates offers advantages including bounded weights, high prediction accuracy, fast convergence rate, low training uncertainty, low computational cost, and ease of hyperparameter tuning.
daForest improves deep forest ensemble for classification with better performance.
problem Improving classification accuracy with small training sets.
method SAMME.R boosting, feed-forward connection, hyper-parameters optimization.
result daForest outperforms neural networks and achieves state-of-the-art results.
Framework improves gradient estimation for faster training convergence.
problem Efficiently estimating noisy gradients in stochastic optimization.
method Dynamic adaptive importance sampling combining multiple distributions.
result Adaptively weighted multiple importance sampling yields superior gradient estimates.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…
Proposes a new tensor decomposition method for functional temporal data with adaptive complexity.
problem Challenges in temporal tensor decomposition for general tensor data with continuous indexes.
method Encodes continuous spatial indexes as learnable Fourier features and uses neural ODEs for temporal trajectories. Introduces a sparsity-inducing prior for complexity adaptation.
result Significantly outperforms existing methods in prediction performance and robustness against noise.
PSC classifier improves HDLSS classification on class-imbalanced data.
problem Classification on high-dimension low-sample-size data with class imbalance.
method Population Structure-learned Classifier (PSC) maximizing inter-class and intra-class scatter matrices.
result PSC outperforms state-of-the-art methods on IHDLSS.
Paper develops online learning-based risk-averse MPC for uncertain systems.
problem Designing robust MPC for systems with unknown but inferable stochastic disturbances.
method Proposes a novel online learning framework using CVaR constraints and Dirichlet process mixture models.
result Demonstrates improved robustness and adaptability of MPC in handling time-varying disturbance distributions.