Online learning improves big data accuracy quickly.
problem Heterogeneity in big data analysis.
method Online machine learning for big data.
result Online learning converges quickly to batch accuracy.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “learning accuracy”
Machine learning can predict cancer with 100% accuracy on a dataset.
problem Accuracy of cancer predictions using machine learning.
method Extensive experiments on the Wisconsin Diagnostic Breast Cancer dataset.
result Machine learning algorithms can be easily misled to achieve 100% accuracy.
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show …
Learning ReLU networks to high uniform accuracy requires exponentially many samples.
problem Achieving high uniform accuracy on ReLU networks for security-critical applications.
method Quantified the number of training samples needed for any algorithm to guarantee uniform accuracy.
result The minimal number of training samples scales exponentially with network depth and input dimension.
Study examines how imputation accuracy affects prediction accuracy in regression problems with missing covariates.
problem Missing covariates in regression or classification problems.
method Simulation and empirical analysis using UCI datasets and statistical inference.
result Imputation accuracy impacts prediction accuracy, especially with Machine Learning methods.
Efficiently tunes hyperparameters with dynamic accuracy method.
problem Optimizing machine learning hyperparameters with inexact evaluations.
method Dynamic accuracy derivative-free optimization for hyperparameter tuning.
result Demonstrates robust and efficient hyperparameter tuning compared to fixed accuracy methods.
This paper analyzes the trade-off between accuracy and communication in personalized federated learning.
problem The accuracy-communication trade-off in personalized federated learning.
method The paper provides a quantitative characterization of the personalization degree on the trade-off, establishing minimax optimality.
result The paper offers theoretical insights for choosing the personalization degree and validates the results on synthetic and real-world datasets.
FADE framework improves fairness and accuracy in ensemble learning.
problem Improving fairness in existing models without sacrificing accuracy.
method Flexible fair ensemble learning framework targeting multiple fairness criteria.
result Multiple unfairness measures can be minimized simultaneously with little impact on accuracy.
Paper improves privacy-accuracy balance in federated learning.
problem Privacy-accuracy tradeoffs in federated learning.
method Personalized federated learning with joint differential privacy.
result Coordination of local and centralized learning improves accuracy while maintaining privacy.
Improves natural accuracy of deep learning models by combining robust predictions and features.
problem Maintaining natural accuracy while resisting adversarial attacks.
method Ensemble methods combining robust and standard models.
result Optimized natural accuracy through ensemble of robust models.
New methods show robustness and accuracy can coexist.
problem Inevitability of robustness-accuracy tradeoff in deep learning.
method Prove robustness and accuracy achievable through locally Lipschitz functions; explore combining dropout with robust training methods.
result Achieving robustness and accuracy requires methods imposing local Lipschitzness and deep learning generalization techniques.
BitPruning learns optimal bitlengths for neural networks to balance accuracy and efficiency.
problem Finding the minimum bitlength for neural network accuracy.
method A training method that penalizes large bitlengths and minimizes other quantifiable criteria.
result The method learns efficient representations while maintaining accuracy, reducing bitlengths by 3.76 bits on average per layer.
Study shows accuracy of neural networks depends more on error location than percentage of error.
problem Effect of noise on accuracy in incremental learning neural networks.
method Empirical study using Perceptron, Feed Forward Neural Network, and Radial Basis Function Neural Network.
result Accuracy of neural networks is more dependent on error location than the percentage of error.
The paper tackles fairness and accuracy in ML models under domain shifts.
problem Designing fair and accurate ML models that perform well in unseen domains.
method Theoretical bounds and sufficient conditions for fairness and accuracy transfer under domain generalization.
result A learning algorithm that ensures fair and accurate models even when deployment environments change.
New active learning strategy improves decision-making accuracy.
problem Maximizing decision-making accuracy in sequential data acquisition.
method Introduces a novel active learning criterion that maximizes expected information gain on the posterior decision distribution.
result Improved performance in decision-making accuracy compared to existing alternatives.
New framework learns interpretable rule ensembles without sacrificing accuracy.
problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systemsmath.DS
This paper proves long-time accuracy of ensemble Kalman filters for chaotic and machine-learned systems.
problem Ensuring long-term accuracy of ensemble Kalman filters for complex dynamical systems.
method Established conditions for long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems.
result Ensemble Kalman filters maintain small estimation error over long time horizons for chaotic and machine-learned systems.
AFLAC improves domain generalization by balancing invariance and accuracy.
problem Balancing domain invariance and classification accuracy for domain generalization.
method Adversarial feature learning with accuracy constraint (AFLAC).
result AFLAC outperforms domain-invariance-based methods on synthetic and real-world datasets.
New techniques improve 16-bit training accuracy without 32-bit units.
problem Training deep learning models with only 16-bit floating-point units.
method Studied BFloat16 units and applied stochastic rounding and Kahan summation techniques.
result Up to 7% absolute validation accuracy gain in 16-bit-FPU training.
Paper analyzes trade-offs in top-k classification accuracies and proposes a new loss function.
problem CE loss does not always optimize top-k prediction, especially with complex data.
method Introduces a novel top-k transition loss to improve top-k accuracy.
result Our loss function improves top-k accuracy, especially for k > 10.
Paper proposes AdaBoost-assisted ELM for efficient online sequential classification.
problem Efficient online sequential classification with improved accuracy and stability.
method Utilizes AdaBoost for cost-sensitive learning and forgetting mechanism for stability.
result Achieves 94.41% accuracy on MNIST dataset with reduced standard deviation.
SGD with large learning rates can achieve better test accuracy than expected.
problem SGD with large learning rates often outperforms expected convergence bounds.
method Proved that SGD with small learning rates stays close to gradient flow path on modified loss.
result Explicitly adding an implicit regularizer to the loss improves test accuracy.
This paper uses deep reinforcement learning to compress CNN models, reducing size and maintaining accuracy.
problem Reducing model size for efficient deployment on limited hardware resources.
method Two-stage compression pipeline: pruning and quantization using deep reinforcement learning.
result Significant reduction in model size with minimal loss in accuracy.
New research shows no trade-off between fairness and accuracy in machine learning.
problem The trade-off between fairness and accuracy in machine learning is a widely accepted belief.
method Using mismatched hypothesis testing and Chernoff information, the study demonstrates that optimal fairness and accuracy can be achieved simultaneously.
result There is no inherent trade-off between fairness and accuracy in ideal distributions, but it exists when measured with respect to biased datasets.
Enhances machine learning accuracy with multi-level training data.
problem Improving accuracy of machine learning algorithms for differential equations.
method Combining coarse and fine resolution training data.
result Significant gains in accuracy over single-level algorithms.
A hierarchical approach improves classification accuracy in large datasets.
problem Improving classification accuracy in large datasets with high dimensionality.
method Hierarchical subspace learning to scale manifold learning methods.
result Average 5% increase in classification accuracy.
Deep neural networks bring in impressive accuracy in various applications, but the success often relies on the heavy network architecture. Taking well-trained heavy networks as teachers, classical teacher-student learning paradigm aims to learn a student network that is lightweight yet accurate. In this way, a portable…
Enhances FL with domain adaptation for better model accuracy.
problem Improving model accuracy in Federated Learning.
method Domain adaptation for per-user model improvement in Federated Learning.
result Domain adaptation improves model accuracy for all users, especially under differential privacy constraints.
This paper explores normalization in neural ODEs, achieving high accuracy in CIFAR-10.
problem Understanding the role of normalization in neural ODEs.
method Investigated different normalization techniques and their impact on neural ODEs performance.
result Achieved 93% accuracy in CIFAR-10 classification task.
Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability d…
Meta-GLAR combines global deep representations with local adaptation for improved forecasting accuracy.
problem Joint learning from related time series boosts accuracy but fails for out-of-sample forecasting.
method Meta-GLAR uses a meta-learning approach to adapt RNN representations for each time series.
result Meta-GLAR outperforms state-of-the-art methods in out-of-sample forecasting accuracy.
Study shows skewed data labels significantly impact decentralized ML accuracy.
problem Skewed data labels across devices/locations cause significant accuracy loss in decentralized ML.
method Detailed experimental study on skewed data labels, presenting SkewScout system-level approach.
result Skewed data labels are a fundamental challenge for decentralized learning, affecting many applications and models.
The paper analyzes the tradeoffs between accuracy and invariance in learning representations.
problem Achieving both accuracy and invariance in machine learning models.
method Information theoretic analysis of classification and regression settings.
result Characterization of the accuracy and invariance achievable by any representation of the data.
Astraea improves federated learning accuracy on imbalanced data.
problem Accuracy degradation in federated learning due to imbalanced data distribution.
method Self-balancing federated learning framework with data augmentation and client rescheduling.
result Astraea shows +5.59% and +5.89% improvement in top-1 accuracy on imbalanced datasets.
A method improves deep network accuracy with low precision quantization.
problem Maintaining high accuracy in low precision deep networks.
method Learned Step Size Quantization, improving quantizer configuration and gradient estimation.
result Achieves highest accuracy on ImageNet with 2-4 bit precision models.
Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most effic…
RIFLE improves deep transfer learning by reinitializing fully-connected layers.
problem Limited improvement in transfer learning accuracy with pre-trained models on small datasets.
method Re-Initializing fully-connected layers with random scratch during fine-tuning.
result Significant improvement in deep transfer learning accuracy across various datasets.
Study shows fine-tuning outperforms few-shot learning algorithms in low-resolution datasets.
problem Learning novel classes with limited examples in machine learning.
method Fine-tuning networks with low learning rates and adaptive optimizers, updating entire network for domain shifts.
result Fine-tuning achieves higher accuracy than common few-shot learning algorithms in 1-shot and 5-shot tasks.
This paper improves deep learning models' accuracy with differential privacy using gradient encoding and denoising.
problem Deep learning models leak sensitive information about their training datasets.
method Gradient encoding to map gradients to a smaller vector space, and denoising for post-processing.
result Our technique achieves better model accuracy with differential privacy guarantees compared to state-of-the-art methods.
Improved accuracy with fewer labels using MixMatch and active learning.
problem Achieving high accuracy with limited labeled data.
method Combining MixMatch and active learning techniques.
result Significant improvement in accuracy on CIFAR-10, CIFAR-100, and SVHN datasets.
New activation improves deep learning accuracy and robustness.
problem Improving accuracy and robustness of deep neural nets with limited data.
method Replaces softmax with graph Laplacian-based interpolating function.
result Significantly improves natural and robust accuracy.
New RL method optimizes power and accuracy for activity recognition.
problem Balancing power consumption and accuracy in wearable devices for activity recognition.
method Reinforcement Learning with multiple feedback sources for feature selection.
result Achieved good trade-off between power consumption and accuracy.
We improve private training accuracy with learning rate schedules and matrix factorizations.
problem Private training with learning rate schedules and correlated noise.
method General upper and lower bounds for learning rate schedules, memory-efficient constructions, and schedule-aware factorizations.
result Schedule-aware factorizations improve accuracy in private training.
SliceOut speeds up deep learning training without sacrificing accuracy.
problem Frequent model re-training and large model training workloads in deep learning.
method SliceOut uses dropout-inspired scheme to drop contiguous sets of units at random, leveraging GPU memory layout.
result 10-40% speedups and memory reduction with minimal accuracy loss.
Contrast trees assess machine learning accuracy, boosting improves performance.
problem Lack of accuracy assessment for machine learning results.
method Contrast trees and distribution boosting methods.
result Distribution boosting provides an assumption-free method for estimating outcome distributions.
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…
This paper explores how diverse neural network ensembles improve prediction accuracy and robustness against deception.
problem Improving prediction accuracy and robustness of neural networks against adversarial attacks.
method Examines and measures ensemble diversity, develops algorithms for creating and combining diverse ensembles.
result Greater diversity in neural network ensembles leads to higher accuracy and robustness against deception.
Flexible deep learning models for dynamic accuracy and speed trade-offs.
problem Dynamic accuracy and speed trade-offs in real-world applications.
method Training deep neural networks with a new method allowing flexible numerical precision during inference.
result Achieved comparable accuracy to dedicated models trained at the same precision with dynamic precision settings.