This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In this work, in order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) …
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.
A method to reduce memory usage in NAS by pruning the search space.
problem High GPU memory consumption in One-Shot NAS techniques.
method Utilising Zero-Shot NAS to prune the search space before applying One-Shot NAS.
result Reduces memory consumption by 81% while maintaining accuracy.
TGLS generates text by optimizing search results and learning from them.
problem Unsupervised text generation without explicit training data.
method TGLS uses simulated annealing to estimate sentence quality, then a conditional generative model learns from search results.
result TGLS outperforms unsupervised baselines and matches state-of-the-art supervised methods in paraphrase generation.
Paper presents a new trie for integer sketches to improve similarity searches.
problem Efficient similarity searches on integer sketches.
method Introduces a novel b-bit sketch trie that leverages succinct data structures. result Significantly improves search time and space-efficiency of similarity searches.
ImmuNeCS uses AI immune system to build neural committees for efficient deep learning model creation.
problem High computational cost and bias in Neural Architecture Search.
method Neural Committee Search using an artificial immune system to balance diversity and performance.
result ImmuNeCS consistently outperforms random search and ensembles yield better results within reasonable GPU budgets.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
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.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
In neural architecture search, the structure of the neural network to best model a given dataset is determined by an automated search process. Efficient Neural Architecture Search (ENAS), proposed by Pham et al. (2018), has recently received considerable attention due to its ability to find excellent architectures with…
Improves diffusion model performance and efficiency through classical search.
problem Tackles inference-time control in diffusion models.
method Proposes a framework combining local and global search for efficient navigation.
result Significant gains in performance and efficiency across various domains.
In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable …
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…
New metrics solve machine learning limitations.
problem Previous success metrics restrict application to specific forms of machine learning.
method Define decomposable metrics as linear operations on probability distributions.
result Demonstrated theorems bounding success in various ways, generalizing existing results.
Study reduces NAS search cost by generating multiple complex architectures in one shot.
problem Finding multiple neural architectures with varying complexities efficiently.
method Uses importance sampling to generate and update multiple distributions of architectures.
result Reduces search cost by finding multiple architectures with different complexities in a single search.
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users speci…
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
einspace expands NAS search space to include diverse neural architectures.
problem NAS results are often limited to existing structures; new designs are rare.
method einspace uses a probabilistic context-free grammar to create a versatile search space.
result einspace discovers novel and improved architectures, including convolutions and attention.
Paper evaluates robustness of NAS against poisoning attacks.
problem Robustness of Neural Architecture Search (NAS) against poisoning attacks.
method Evaluation of Efficient NAS (ENAS) against carefully designed ineffective operations in poisoning attacks.
result Demonstrates how poisoning attacks exploit design flaws in ENAS controller.
Optimizes Airbnb pricing to increase revenue and reduce booking regret.
problem Maximizing revenue in an online marketplace with competing products.
method Two-stage model to price retrieved items based on learned distributions of their values.
result Improves revenue and booking regret by at least +20% and +55% respectively.
UNAS combines DNAS and RL for efficient architecture search.
problem Discovering high accuracy or low latency neural architectures.
method Unified framework combining differentiable and reinforcement learning approaches.
result UNAS achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets.
This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding spac…
Adapts large transformer model for search query intent understanding.
problem Understanding and predicting user intents from search queries.
method Adapts BERT-like architecture for search queries, accounts for query noisiness and sparseness.
result Builds a shareable deep learning model for query intent identification.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different…
Neural A* uses machine learning to improve path planning efficiency.
problem Challenges in applying machine learning to search-based path planning.
method Reformulated A* search as a differentiable network coupled with a convolutional encoder.
result Neural A* outperforms state-of-the-art planners in optimality and efficiency.
Paper tackles diversity in Airbnb search results.
problem Show diverse results to improve user experience.
method Heuristic based approaches → Deep learning solution using RNNs.
result Novel deep learning solution improves diversity in search results.
Simplified NAS for GNN architectures improves efficiency and expressiveness.
problem Efficient and effective discovery of optimal GNN architectures.
method SNAG framework with a novel search space and reinforcement learning.
result SNAG framework outperforms human-designed and existing NAS methods.
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based on their results on the downstream task. While intuitive, …
FLOP algorithm speeds up causal structure learning for linear models.
problem Efficiently learning causal structures from discrete data.
method FLOP algorithm combines fast parent selection and iterative score updates.
result FLOP finds highly accurate causal structures with near-perfect recovery.
A new multilabel classification framework improves ANN search performance.
problem Efficiently finding approximate nearest neighbors in large datasets.
method Formulated ANN search as a multilabel classification problem, using partitioning classifiers.
result Natural classifier leads to strictly improved performance in ANN search.
Guided Evolution improves NAS efficiency and accuracy.
problem NAS methods converge to local minima and are complex.
method G-EA: guided evolutionary approach with initialization evaluation and continuous knowledge extraction.
result G-EA achieves state-of-the-art results in CIFAR-10, CIFAR-100, and ImageNet16-120.
New algorithms improve contextual search in the presence of adversarial corruptions.
problem Improving search accuracy in dynamic pricing settings with corrupted responses.
method Two algorithms based on binary search and gradient descent methods.
result Achieve near-optimal regret in the absence of adversarial corruptions and gracefully degrade with corrupted agents.
Robotics improves by using image search to solve new tasks.
problem Generalization in robotics.
method Combining visual and textual information to demarcate intended word meaning.
result Our approach leads to improved results compared to Google searches, treating the problem of polysemes.
SSNAS finds neural architectures without labeled data.
problem Limited labeled data for NAS.
method Self-supervised learning for NAS.
result Comparable results to supervised NAS with labeled data.
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and …
A new language for neural architecture search decouples search spaces and algorithms.
problem Current neural architecture search methods are limited to specific use-cases and lack general-purpose constructs.
method Proposes a formal language for encoding search spaces over general computational graphs, allowing modular, composable, and reusable encodings.
result The language enables easy experimentation with different search spaces and algorithms without reinventing the wheel.
We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human ex…
Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between feat…
AutoOD automates outlier detection using curiosity-guided search and self-imitation learning.
problem Automated outlier detection for complex tasks with big data.
method Curiosity-guided search strategy and self-imitation learning.
result AutoOD identifies optimal neural network models with superior performance.
In deep learning, performance is strongly affected by the choice of architecture and hyperparameters. While there has been extensive work on automatic hyperparameter optimization for simple spaces, complex spaces such as the space of deep architectures remain largely unexplored. As a result, the choice of architecture …
Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient exp…
DrNAS improves neural architecture search with Dirichlet distribution and progressive learning.
problem Efficiently search for neural architectures with improved generalization and exploration.
method Formulates architecture search as a distribution learning problem using Dirichlet distribution and gradient-based optimization. Introduces a progressive learning scheme to handle large-scale tasks.
result Achieves state-of-the-art results on CIFAR-10 and ImageNet, demonstrating improved generalization and exploration.
This research explores various sampling methods and probability distributions for hard alignment in sequence-to-sequence TTS synthesis.
problem Improving alignment accuracy in sequence-to-sequence text-to-speech synthesis.
method Investigated various sampling methods (greedy, beam, random) and probability distributions (Bernoulli, Concrete) for hard alignment.
result Deterministic search is more preferable than stochastic search for natural alignment transition.
The neural architecture search (NAS) algorithm with reinforcement learning can be a powerful and novel framework for the automatic discovering process of neural architectures. However, its application is restricted by noncontinuous and high-dimensional search spaces, which result in difficulty in optimization. To resol…
Study compares variable selection methods for model evaluation and search.
problem Understanding underlying mechanisms in scientific questions through variable selection.
method Comprehensive comparison of BIC and AIC for model evaluation and various search methods (exhaustive, greedy, LASSO path, stochastic search) for model space exploration.
result Exhaustive search BIC and stochastic search BIC outperform other methods in small and large model spaces, respectively, improving correct identification rate and reducing false discovery rate.
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.
RARTS improves neural architecture search efficiency and accuracy.
problem Efficiency and accuracy issues in first-order DARTS.
method Formulates a relaxed architecture search (RARTS) method that uses whole dataset splitting.
result RARTS outperforms DARTS and its variants in accuracy and efficiency.