SNAS efficiently searches neural architectures using stochastic optimization.
problem Efficiently searching for optimal neural architectures.
method SNAS trains parameters of both neural operations and architecture distribution in a single round of backpropagation, using a novel search gradient and locally decomposable rewards.
result SNAS achieves state-of-the-art accuracy with fewer training epochs compared to other NAS methods.
Analyzes SVM classifier behavior with different parameters and data types.
problem Tuning SVM parameters for balanced and imbalanced data.
method Behavioral analysis of SVM with different parameters and data types, proposing a novel search algorithm.
result Proposed search algorithm reduces computational time and provides expected kernel function range.
A new Randomized-Hyperopt method improves XGBoost hyperparameter tuning.
problem Improving the performance of XGBoost through hyperparameter optimization.
method Proposes Randomized-Hyperopt for XGBoost hyperparameter tuning.
result Randomized-Hyperopt outperforms other methods in terms of accuracy and execution time.
AGNN automates GNN architecture search, achieving best performance.
problem Finding optimal GNN architectures is laborious and requires human expertise.
method AGNN uses reinforcement learning to search for optimal GNN architectures within a predefined space, with a novel parameter sharing strategy.
result AGNN identifies optimal GNN architectures achieving best performance.
New ShuffleNASNets improve efficiency and speed of CNN models.
problem Complexity and inefficiency of search-based neural architecture designs.
method Adopted and enhanced Efficient Neural Architecture Search (ENAS) with ShuffleNet V2 principles.
result Achieved significantly less complex, faster, and more efficient CNN models.
Bonsai-Net efficiently discovers state-of-the-art models with fewer parameters.
problem Efficiently discovering state-of-the-art neural architectures with minimal computational expense.
method Bonsai-Net uses a modified differential pruner to explore a relaxed search space.
result Bonsai-Net consistently discovers better architectures than random search with fewer parameters.
Differentiable NAS method optimizes network architecture and parameters efficiently.
problem Challenging to simultaneously guarantee effectiveness and efficiency in network architecture search.
method Differentiable architecture search with ensemble Gumbel-Softmax estimator.
result End-to-end mechanism for searching network architectures, discovering high-performance architectures efficiently.
A new method optimizes neural sequence models for better task performance.
problem Training neural sequence models with maximum likelihood estimation ignores task losses.
method Maximum likelihood guided parameter search (MGS) in the parameter space.
result MGS optimizes sequence-level losses, reducing repetition and non-termination.
Paper stabilizes DARTS algorithm for better neural architecture search.
problem Weak stability of DARTS algorithm leading to unreliable results.
method Amended gradient estimation method to bridge optimization gap.
result Significant improvement in search stability and larger search spaces explored.
Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application o…
Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve t…
Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with…
A hybrid RL-Bayesian search configures machine learning pipelines efficiently.
problem Optimizing hyper-parameters in a hierarchical conditional space.
method Combines Reinforcement Learning and Bayesian Optimization.
result Outperforms state-of-the-art methods in pipeline optimization.
Automated spectral clustering algorithm discovers clusters without parameter tuning.
problem Automatic spectral clustering for multi-scale data.
method Heuristic iterative eigengap search with global and local scaling.
result Discover different patterns with accuracy >90% in most cases.
BNAS improves neural architecture search with a scalable, fast, and efficient approach.
problem Efficiently searching for optimal neural architectures with high performance and low training time.
method Designing a broad scalable architecture (BCNN) with reinforcement learning and parameter sharing, and developing two variants.
result Significantly reduces training time and achieves state-of-the-art performance on CIFAR-10 and ImageNet.
WRS improves CNN hyperparameter optimization.
problem Finding optimal hyperparameters for CNNs efficiently.
method Combination of Random Search and probabilistic greedy heuristic.
result WRS outperforms other methods in CNN hyperparameter optimization.
Multi-model forgetting occurs when training multiple deep networks sequentially, leading to performance degradation of previously trained models.
problem Performance degradation of previously trained models when sequentially training multiple deep networks with shared parameters.
method Introduce a weight plasticity loss that regularizes the learning of shared parameters based on their importance for previous models.
result Weight plasticity loss effectively preserves the performance of previously trained models during sequential training and neural architecture search.
BayesNAS uses Bayesian learning to improve neural architecture search efficiency.
problem Improper treatment of zero operations and architecture parameter pruning issues in one-shot NAS methods.
method Employing hierarchical automatic relevance determination (HARD) priors for Bayesian learning to model architecture parameters.
result Found architecture on CIFAR-10 in just 0.2 GPU days using a single GPU.
In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures with an unknown number of layers. To relate performance data gathered for different architectures, we define a new kernel for conditional p…
PARMESAN learns from memory without parameters for fast, efficient continual learning.
problem Inflexibility in deep learning methods for continual learning.
method Transductive reasoning and memory search for parameter-free learning.
result 3-4 orders of magnitude faster than baselines, comparable performance.
Paper studies kernel hyperparameters for clustering, proposing an efficient search method.
problem Challenges in tuning kernel parameters for clustering, especially for RBF kernels.
method Derives a lower bound for RBF kernel parameters, proposes an efficient hyperparameter search algorithm.
result Proposes an efficient algorithm for hyperparameter search in kernel clustering, improving upon grid search.
EffAcTS uses active learning to select model parameters for robust policy search.
problem Learning robust policies that perform well in unseen environments.
method EffAcTS is an active learning framework that selects model parameters using Linear Bandits to minimize data collection.
result Efficiency gains in sample collection and improved policy performance.
Optimal search for change point anomaly in multiple processes.
problem Detecting a change point in an anomalous process among multiple normal processes.
method Deterministic search algorithm balancing sample complexity and detection accuracy.
result Asymptotically optimal in minimizing Bayes risk.
Bayesian optimization tackles unknown search spaces with automatic expansion.
problem Bayesian optimization in unknown search spaces is challenging.
method Proposes a systematic volume expansion strategy to find points close to the objective function maximum without specifying parameters.
result Derives analytic expressions for expansion triggers and sizes, achieving epsilon-accuracy after a finite number of iterations.
Paper optimizes energy-based controller for swinging up a pendulum using entropy search.
problem Finding optimal parameters for energy-based controllers is hard.
method Bayesian optimization (Entropy Search) applied to energy-based controller design.
result Optimal controller improves performance of a swinging pendulum.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
Evolved Transformer improves on Transformer architecture for language tasks.
problem Improving Transformer architecture for sequence tasks.
method Evolutionary architecture search with warm starting and dynamic resource allocation.
result Evolved Transformer achieves state-of-the-art BLEU scores and reduces parameter count.
This work improves Bayesian Optimization for setting DNN hyper-parameters.
problem Manual setting of DNN hyper-parameters is error-prone and computationally expensive.
method Combines Bayesian Optimization with tuning rules to reduce search space and improve accuracy.
result Improves efficiency and accuracy of hyper-parameter tuning for deep neural networks.
ENAS discovers efficient neural architectures quickly and cheaply.
problem Automatic model design and neural architecture search.
method Parameter sharing and policy gradient for subgraph selection.
result ENAS discovers novel architectures achieving state-of-the-art performance.
HMCNAS generates competitive neural architectures without human-defined parameters.
problem Lack of human-defined parameters in Neural Architecture Search.
method Combines Hidden Markov Chains and Bayesian Optimization for autonomous search space generation and competitive model generation.
result HMCNAS generates competitive models in a short time without human-defined parameters.
DONNA rapidly finds optimal neural networks across diverse spaces.
problem Efficient scaling and handling of diverse architectural search-spaces in NAS.
method Three-phase pipeline: accuracy predictor, rapid evolutionary search, and optimal model finetuning.
result 100x faster than MNasNet in finding state-of-the-art architectures on-device.
Tree-search algorithm optimizes noisy black-box functions with multi-fidelity queries.
problem Optimizing noisy functions with expensive evaluations and low-cost approximations.
method Tree-like hierarchical partitions and multi-fidelity bandit tree-search algorithm.
result Simple regret bounds for the proposed algorithm.
Efficient neural architecture search by sampling structure and operations.
problem Efficiently searching for optimal neural architectures.
method Decouples structure and operation search, using reinforcement learning with policy vectors.
result Significantly improved efficiency compared to traditional methods.
Automatically finds efficient multi-task models with less data.
problem Training separate models requires more data, parameters, and time.
method Compact search space for multi-task architectures, feature distillation for quick evaluation.
result Automatically identifies multi-task architectures that balance resource requirements and performance.
The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves …
Tuning SVM and boosting models using optimization algorithms.
problem Tuning parameters for SVM and boosting models across various datasets.
method Used grid search to identify parameter ranges and optimization algorithms to select models.
result Optimization algorithms outperformed grid search in selecting well-performing models.
AutoShrink optimizes neural architectures by shrinking cell structures.
problem Resource constraints in deploying DNNs on mobile devices.
method Topology-aware node-based Neural Architecture Search (NAS).
result AutoShrink achieves up to 48% parameter reduction and 34% MACs savings.
Optimizes SGD for text classification using hyper-parameter tuning.
problem Improving performance of SGD in text classification.
method Fine-tuning hyper-parameters using Grid-Search approach.
result Optimized SGD classification leads to better accuracy and execution time.
FiGS searches over a larger space of architectures for efficient mobile models.
problem Designing small, efficient deep networks for mobile devices.
method Differentiable search method using sparse regularization and Logistic-Sigmoid distribution.
result FiGS produces state-of-the-art parameter-efficient models on ImageNet and improves object detection performance.
A new method selects algorithms and optimizes their hyper-parameters efficiently.
problem Redundant hyper-parameter search space in AutoML.
method Cascaded algorithm selection and hyper-parameter optimization with ER-UCB bandit.
result ER-UCB strategy achieves optimal regret bound for algorithm selection.
New algorithms improve neural architecture search with faster convergence.
problem Improving efficiency and accuracy of neural architecture search.
method Geometry-aware gradient algorithms to optimize continuous relaxation of discrete search spaces.
result Exceeds state-of-the-art results on CIFAR and ImageNet benchmarks.
SubSearch detects graph outliers and estimates SBM parameters robustly.
problem Real-world graphs often deviate from ideal SBM assumptions.
method Subgraph search to find subgraphs that align with SBM assumptions.
result SubSearch accurately estimates SBM parameters and detects outliers.
Improved similarity search in embeddings using InfoNCE loss.
problem Improving similarity search in embedding models trained by contrastive learning.
method Introduced a new continuity bound for InfoNCE loss via Gâteaux differentiation, preserving the averaging effect of negative samples.
result Demonstrated that the averaging effect of k negative samples in InfoNCE loss carries over to stabilisation of generalisation error as k grows. Deep-n-Cheap automates deep learning model search for low complexity.
problem Finding efficient deep learning models for various datasets.
method Automated search framework for architecture and hyperparameters, including search transfer.
result Models offer comparable performance to state-of-the-art but are faster to train.
Meta-learning finds the best model in the space of all possible models.
problem No single learning algorithm performs best on all data.
method Searches for the best combination of parameters and procedures in the space of all possible models.
result Meta-learning approach finds the best method in all cases.
Dynamic Transfer Networks improve NER from clinical notes with less data.
problem Improving named entity recognition from clinical notes with limited data.
method Dynamic Transfer Networks (DTN) using parameter sharing and gating architecture.
result DTN achieves better NER performance with fewer training settings.
A neural network approach to Monte-Carlo tree search.
problem Improving tree search algorithms for planning problems.
method Learning neural network architecture to control search parameters.
result The learned search algorithm outperformed traditional MCTS.
EENA efficiently searches neural architectures with minimal resources.
problem Lack of direction and high computational cost in neural architecture search.
method EENA uses guided evolution with mutation and crossover operations.
result EENA designs highly effective neural architectures with minimal resources.