Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility
Bandwidth forecasting in Mobile Broadband (MBB) networks is a challenging task, particularly when coupled with a degree of mobility. In this work, we introduce HINDSIGHT++, an open-source R-based framework for bandwidth …
AutoMLHyperparameter OptimizationAdversarial Training for EM Classification Networks
We present a novel variant of Domain Adversarial Networks with impactful improvements to the loss functions, training paradigm, and hyperparameter optimization. New loss functions are defined for both forks of the DANN n…
ClassificationGeneral ClassificationHyperparameter OptimizationMOFA: Modular Factorial Design for Hyperparameter Optimization
This paper presents a novel and lightweight hyperparameter optimization (HPO) method, MOdular FActorial Design (MOFA). MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter…
Hyperparameter OptimizationModel SelectionConvergence Properties of Stochastic Hypergradients
Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning. A key step to tackle these problems is the effic…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningVEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of…
AutoMLBIG-bench Machine LearningData AugmentationDiversity+3A Critical Assessment of State-of-the-Art in Entity Alignment
In this work, we perform an extensive investigation of two state-of-the-art (SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore, we first carefully examine the benchmarking process and identify…
BenchmarkingEntity AlignmentHyperparameter OptimizationKnowledge GraphsDelta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Hyperparameter optimization of neural networks can be elegantly formulated as a bilevel optimization problem. While research on bilevel optimization of neural networks has been dominated by implicit differentiation and u…
Bilevel OptimizationHyperparameter OptimizationImage ClassificationRolling Shutter CorrectionHyperparameter Transfer Across Developer Adjustments
After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as …
Hyperparameter OptimizationBilevel Optimization: Convergence Analysis and Enhanced Design
Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper, we investigate the nonconvex-strongly-c…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningGenetic-algorithm-optimized neural networks for gravitational wave classification
Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in deve…
ClassificationGeneral ClassificationGravitational Wave DetectionHyperparameter OptimizationA Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough. In practice, an exhaustive search over all possible techniques and hyperparameters is needed to find opt…
Decision MakingFairnessHyperparameter OptimizationNavigateHow Out-of-Distribution Data Hurts Semi-Supervised Learning
Recent semi-supervised learning algorithms have demonstrated greater success with higher overall performance due to better-unlabeled data representations. Nonetheless, recent research suggests that the performance of the…
Hyperparameter OptimizationTimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series
Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective representation of MTS data, it is not only essen…
Anomaly DetectionClusteringHyperparameter OptimizationRepresentation Learning+2LibKGE - A knowledge graph embedding library for reproducible research
LibKGE ( https://github.com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals of LibKGE…
Graph EmbeddingHyperparameter OptimizationKnowledge Graph EmbeddingLink PredictionProvably Faster Algorithms for Bilevel Optimization and Applications to Meta-Learning
Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper, we investigate the nonconvex-strongly-c…
Bilevel OptimizationHyperparameter OptimizationMeta-LearningNon-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
Gradient-based meta-learning has earned a widespread popularity in few-shot learning, but remains broadly impractical for tasks with long horizons (many gradient steps), due to memory scaling and gradient degradation iss…
Few-Shot LearningHyperparameter OptimizationMeta-LearningMulti-Source Unsupervised Hyperparameter Optimization
How can we conduct efficient hyperparameter optimization for a completely new task? In this work, we consider a novel setting, where we search for the optimal hyperparameters for a target task of interest using only unla…
Hyperparameter OptimizationTuning Word2vec for Large Scale Recommendation Systems
Word2vec is a powerful machine learning tool that emerged from Natural Lan-guage Processing (NLP) and is now applied in multiple domains, including recom-mender systems, forecasting, and network analysis. As Word2vec is …
Hyperparameter OptimizationRecommendation SystemsAnisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI
Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional th…
Hyperparameter OptimizationSegmentationA Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine Translation
With neural networks having demonstrated their versatility and benefits, the need for their optimal performance is as prevalent as ever. A defining characteristic, hyperparameters, can greatly affect its performance. Thu…
Hyperparameter OptimizationMachine TranslationTranslation