Global constraints improve cognates detection performance.
problem Improving cognates detection accuracy.
method Rescoring of score matrices using global constraints.
result Significant performance improvements across various datasets.
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.
169,341 papers · 148 categories
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48 results for “improving performance”
Next-Depth Lookahead Treestat.ML
Improves tree model performance by considering future node splits.
problem Improving tree model performance.
method Next-Depth Lookahead Tree (NDLT) model that evaluates future node splits.
result Enhanced tree model performance.
Improving the Predictive Performances of Nearest Neighbors Learning by Efficient Variable Selectionstat.ML
Paper improves -NN predictive performance with efficient variable selection.
problem Improving predictive performance of -NN models.
method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
Study shows how machine learning can improve human performance in deception detection.
problem Improving human performance in critical tasks involving ethical and legal concerns.
method Investigated how machine learning models and their predictions can assist humans in deception detection tasks.
result Explanations and predicted labels from machine learning models can improve human performance in deception detection.
Bagging improves sparse regression performance, especially with reduced sampling ratios.
problem Improving sparse regression performance in low measurement scenarios.
method Generalized Bagging with various bootstrap sampling ratios.
result Bagging outperforms L1 minimization and Bolasso in challenging sparse regression cases.
FIRE PBT improves neural network training by focusing on long-term performance.
problem Greedy decision mechanisms in PBT lead to poor long-term performance.
method FIRE PBT uses a fitness metric to encourage long-term performance over short-term improvements.
result FIRE PBT outperforms PBT on ImageNet and matches hand-tuned learning rates.
This work improves regression performance by using distributional losses, finding better optimization leads to improved generalization.
problem Improving regression performance in reinforcement learning.
method Introduced a novel distributional regression loss and investigated its effects on optimization and generalization.
result The novel distributional regression loss leads to improved prediction accuracy and better optimization.
AutoML uses dataset and algorithm descriptions to improve performance.
problem Improving automated machine learning performance.
method Uses language embeddings to augment AutoML recommendations.
result Zero-shot AutoML system provides good solutions in under a second.
Population Gradients improve performance across data-sets and architectures in object classificationcs.LG
New method improves NN performance across various settings.
problem Improving neural network performance across different datasets and architectures.
method Population Gradients (PG) method to calculate non-local gradient estimates.
result Significantly improves final performance across architectures, data-sets, and hyper-parameters.
Study examines how statistical properties of deep learning representations can be adjusted.
problem Improving performance in deep learning models.
method Investigated eight representation regularization methods, including two new rank regularizers.
result Manipulating statistical properties of representations can indirectly improve model performance.
The paper finds that faster technological improvement leads to faster diffusion of products.
problem The relationship between technological improvement and innovation diffusion is not well understood.
method Empirical tests across multiple products and technologies.
result Faster diffusion for products based on more rapidly improving technological domains.
Paper improves safe policy improvement with estimated baseline policy.
problem Unreliable batch Reinforcement Learning algorithms in real-world applications.
method Apply SPIBB algorithms with an estimated baseline policy.
result Safe policy improvement guarantees over true baseline without direct access.
TuneUp improves GNN training by focusing on hard-to-learn nodes.
problem Sub-optimal training of GNNs on all nodes equally.
method Two-stage training: base GNN + tail node improvement.
result Significant improvement in tail node prediction performance.
MLPerf benchmarks ML training to drive performance improvements.
problem Unique challenges in ML training benchmarks.
method Developed MLPerf to overcome ML training's specific challenges.
result Quantitatively evaluated MLPerf's effectiveness.
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvementcs.NI
Deep RL improves cellular network fault management and performance.
problem Fault management and radio performance improvement in outdoor cellular networks.
method Deep Q-Learning for self-organizing networks fault management.
result The proposed algorithm learns to clear alarms and improve radio performance better than existing methods.
Improves neural network performance by enriching training dataset.
problem Achieving worst-case performance guarantees in neural networks.
method Adapting training dataset during training to reduce worst-case violations.
result Improved worst-case performance guarantees in neural networks.
Improved online neural transducer model matches non-streaming model performance.
problem Significant performance degradation of online neural transducer models.
method Increased attention window, LAS initialization, stronger language models.
result Improved online neural transducer model matches non-streaming model performance.
Improved neural active learning algorithms reduce regret and improve performance.
problem Improving performance and reducing regret in neural active learning for non-parametric streaming data.
method Introducing two new regret metrics and leveraging NNs for both exploitation and exploration. The algorithm uses tailored query decision-makers and full feedback.
result Achieved an instance-dependent regret upper bound improving by a multiplicative factor of and removing the curse of dimensionality.
A simple approach improves performance on both past and future tasks in lifelong learning.
problem Forgetting in lifelong learning, where performance on past tasks degrades when learning new tasks.
method Representation ensembling to improve performance on both future and past tasks.
result Representation ensembling demonstrates both forward and backward transfer across various datasets.
This paper improves combine harvester performance using ANN-PSO hybrid model.
problem Improving performance of combine harvesters to minimize waste and reduce maintenance.
method Proposes a hybrid machine learning model combining artificial neural networks and particle swarm optimization.
result Demonstrates higher accuracy and stability in predicting optimal performance of combine harvesters.
Survey of resampling techniques for improving classification performance in unbalanced datasetsstat.AP
This paper reviews resampling techniques to improve classification in imbalanced datasets.
problem Improving classification performance in datasets with class imbalance.
method Review and study of various resampling techniques.
result Effectiveness of resampling techniques on classification performance.
OLBoost improves online decision tree performance without increasing memory or time costs.
problem Improving predictive performance in online decision trees without high memory or time costs.
method OLBoost applies boosting to small regions of the instances space within online decision tree algorithms.
result OLBoost can significantly improve online learning decision tree performance without increasing tree size.
Improves predictive performance of nested dichotomies.
problem Improving the performance of multi-class classification problems.
method A simple, general method for improving nested dichotomies produced by random subset selection techniques.
result Improves root mean squared error of nested dichotomies.
QTRAN++ improves MARL performance in complex environments.
problem Poor empirical performance of QTRAN in complex environments.
method Stabilizing training objective, removing role separation, and introducing a multi-head mixing network.
result QTRAN++ achieves state-of-the-art performance in the Starcraft Multi-Agent Challenge (SMAC).
A Refined Analysis of UCBVIstat.ML
Improved analysis of UCBVI algorithm with better empirical performance.
problem Improving the UCBVI algorithm's performance and understanding its bounds.
method Refined analysis of UCBVI algorithm with improved bonus terms and regret analysis.
result Improving multiplicative constants in UCBVI bounds enhances empirical performance.
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learningcs.LG
Gradient sparsification enhances privacy-preserving machine learning models.
problem Improving performance of differentially-private machine learning models under privacy constraints.
method Gradient sparsification combined with compressed sensing and additive Laplace noise.
result Gradient sparsification can improve performance of differentially-private machine learning models for small privacy budgets.
Enhances neural networks for regression tasks with minimal learning time increase.
problem Improving performance of neural networks in regression tasks.
method Extends the learning procedure of a neural network to improve its performance without changing the prediction.
result The modified model performs better than the original model with minimal learning time increase.
Improved pre-trained embeddings through effective entropy maximization.
problem Developing high-quality pre-trained embeddings for future tasks.
method E2MC criterion defined in terms of low-dimensional constraints.
result Significant improvement in downstream performance.
SAUNA filters out noisy samples to boost RL performance.
problem Improving RL performance by filtering out non-informative samples.
method SAUNA selects samples based on the fraction of variance explained by the value function, rejecting non-informative transitions.
result SAUNA significantly improves RL performance on benchmark problems.
Improved DP models with active learning on public data.
problem Differentially private models trained on sensitive data degrade performance.
method Fine-tuning DP models through active learning on public data.
result Improved accuracy for DP models while maintaining privacy guarantees.
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
Improves target data performance of adaptive classifiers.
problem Adaptive classifiers perform poorly on target data due to distribution shift.
method Constructs robust discriminant analysis estimators.
result Robust discriminant analysis outperforms non-adaptive classifiers on target data.
Data splitting enhances model performance in overparametrized ridgeless regression.
problem Computational inefficiency in training models with large datasets.
method Data splitting as a regularization technique in overparametrized ridgeless regression.
result Data splitting improves statistical performance and computational complexity.
Framework improves human decision-making by learning representations.
problem Improving human decision-making performance conflated with machine accuracy.
method Mind Composed with Machine framework, incorporating human decision-making model into representation learning.
result Empirically demonstrated successful application to various tasks and representational forms.
Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the abilit…
Study shows pruning datasets can improve machine learning model performance.
problem Improving machine learning model performance through dataset pruning.
method Comparison of different algorithms on unpruned and iteratively pruned datasets.
result Algorithms that perform better on unpruned datasets also perform better on pruned datasets.
KT models improved slightly with synthetic student data.
problem Limited access to real student data and lack of diversity in public datasets.
method Simulated student data using three statistical strategies and tested on KT baselines.
result Synthetic data can lead to similar performance as real data.
Improved GANs mitigate forgetting and collapse issues.
problem Catastrophic forgetting and mode collapse in GANs.
method Contrastive learning and mutual information maximization.
result Significantly stabilizes GAN training and improves performance.
Self-modulation improves GAN performance across various settings.
problem Training GANs is challenging due to inconsistent performance.
method Introduces self-modulation, allowing generator feature maps to adapt to input noise.
result Reduces FID by 5%-35% in empirical studies.
RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clippingcs.LG
Improved natural gradient boosting with leaf number clipping for faster and better performance.
problem Slower training speed and poor performance on large datasets for natural gradient boosting.
method Leaf number clipping regularization to optimize hyperparameters and improve performance.
result Significant improvement in performance and up to 4.85x speed up on various datasets.
RPI combines imitation and reinforcement learning to improve policies efficiently.
problem High sample complexity in reinforcement learning.
method Active interleaving between imitation and reinforcement learning, using oracle queries for exploration.
result RPI outperforms existing methods across various domains.
Developed predictive models for improving programming course performance.
problem Improving student performance in programming courses.
method Used M5P Decision Tree and Linear Regression Classifier on structured questionnaire data.
result Variable-based LRC model produced the best model with least evaluation metrics.
Model predicts web page parallelism for improved browser performance and energy.
problem Improving browser performance and energy usage through parallelism.
method Supervised learning model using web page primitives and parallelism features.
result Model predicts parallelism and optimizes performance and energy usage.
Proposes a method to improve neural architectures reproducibly.
problem Lack of reproducibility in Neural Architecture Transformer (NAT).
method Differentiable Neural Architecture Transformation (DNAT).
result DNAT outperforms NAT and is applicable to various models and datasets.
Training on mixed distributions improves test performance even when components are unrelated.
problem Improving test performance with mismatched training and test distributions.
method Analyzing mixture distributions with different training and test proportions.
result Distribution shift can be beneficial, improving test performance even when components are unrelated.
Improved ECG classification using multi-task learning.
problem Low frequency of rare diagnoses in ECGs.
method Developed a multi-task CNN to classify multiple diagnoses from 12-lead ECGs.
result Adding common classes improves performance on rarer classes.
SIBRE boosts reinforcement learning convergence by rewarding improvement over past performance.
problem Improving the rate of convergence in reinforcement learning.
method SIBRE is a reward shaping approach that rewards improvement over the agent's own past performance.
result SIBRE converges faster and more stably to the optimal policy compared to baseline RL algorithms.
A novel KD classifier improves character recognition performance.
problem Improving character recognition accuracy.
method Kernel-based generative classifier in distortion subspace with iterative kernel selection.
result The KD classifier outperforms existing classifiers and has unique recognition capability.