Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Learning Activation Functions for Sparse Neural Networks
Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially a…
AutoMLHyperparameter OptimizationPython Tool for Visualizing Variability of Pareto Fronts over Multiple Runs
Hyperparameter optimization is crucial to achieving high performance in deep learning. On top of the performance, other criteria such as inference time or memory requirement often need to be optimized due to some practic…
Hyperparameter OptimizationIMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis "You shall know a word by the company it keeps" (Harris, 1954), whereas modern predic…
Hyperparameter OptimizationImage CaptioningImage RetrievalObject+1MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization
Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that mu…
FairnessHyperparameter OptimizationNeural Architecture SearchOptimizing Hyperparameters with Conformal Quantile Regression
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate mode…
Gaussian ProcessesHyperparameter Optimizationquantile regressionregressionNatural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers
The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks su…
Hyperparameter OptimizationSentiment Analysistext-classificationText ClassificationHyperparameter Optimization through Neural Network Partitioning
Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improve performance -- especially in the prese…
Federated LearningHyperparameter OptimizationALMERIA: Boosting pairwise molecular contrasts with scalable methods
Searching for potential active compounds in large databases is a necessary step to reduce time and costs in modern drug discovery pipelines. Such virtual screening methods seek to provide predictions that allow the searc…
Activity PredictionDrug DiscoveryHyperparameter OptimizationModel SelectionQuantum Gaussian Process Regression for Bayesian Optimization
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a h…
Bayesian OptimizationGaussian ProcessesHyperparameter OptimizationregressionNatural Evolution Strategy for Mixed-Integer Black-Box Optimization
This paper proposes a natural evolution strategy (NES) for mixed-integer black-box optimization (MI-BBO) that appears in real-world problems such as hyperparameter optimization of machine learning and materials design. T…
Hyperparameter OptimizationTree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
Recent advances in many domains require more and more complicated experiment design. Such complicated experiments often have many parameters, which necessitate parameter tuning. Tree-structured Parzen estimator (TPE), a …
Bayesian OptimizationHyperparameter OptimizationLow-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
We propose an evolution strategies-based algorithm for estimating gradients in unrolled computation graphs, called ES-Single. Similarly to the recently-proposed Persistent Evolution Strategies (PES), ES-Single is unbiase…
Hyperparameter OptimizationPED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces
The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong models. In turn, designing a good HP sp…
Hyperparameter OptimizationRobust Stability of Gaussian Process Based Moving Horizon Estimation
In this paper, we introduce a Gaussian process based moving horizon estimation (MHE) framework. The scheme is based on offline collected data and offline hyperparameter optimization. In particular, compared to standard M…
Hyperparameter OptimizationHPN: Personalized Federated Hyperparameter Optimization
Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and challenging problems. However, existing wor…
Federated LearningHyperparameter OptimizationAutoRL Hyperparameter Landscapes
Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results …
AutoMLHyperparameter OptimizationOpen-Ended Question AnsweringReinforcement Learning (RL)Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to address the challenges of fine-tuning large la…
Hyperparameter Optimizationparameter-efficient fine-tuningTetra-AML: Automatic Machine Learning via Tensor Networks
Neural networks have revolutionized many aspects of society but in the era of huge models with billions of parameters, optimizing and deploying them for commercial applications can require significant computational and f…
Bayesian OptimizationHyperparameter OptimizationModel CompressionNeural Architecture Search+3Deep Ranking Ensembles for Hyperparameter Optimization
Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the res…
Hyperparameter OptimizationLearning-To-RankHyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient h…
BenchmarkingHyperparameter Optimization