Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: Beyond Manual Tuning of Hyperparameters
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Hyperparameter Importance Across Datasets
Sample size selection in optimization methods for machine learning
On optimization methods for deep learning
Learned Optimizers that Scale and Generalize
Parameters optimization of deep learning models using Particle swarm optimization
Bayesian Optimization with Gradients
Bayesian Optimization in a Billion Dimensions via Random Embeddings
A Bayesian Perspective on Generalization and Stochastic Gradient Descent
Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models
Tuning as Ranking
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA
On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning
High Dimensional Bayesian Optimisation and Bandits via Additive Models
Choosing Multiple Parameters for Support Vector Machines
Practical Bayesian optimization in the presence of outliers
Stochastic Gradient Descent With Hyperbolic-Tangent Decay on Classification
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
Transformation invariance in pattern recognition: Tangent distance and propagation
Identifying Cognates By Phonetic And Semantic Similarity
Efficient Implementation of AES Algorithm in FPGA Device
Incomplete tooth fracture – proposal for a new definition
Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers