{"total":59,"rows":[{"id":"2301.08727","title":"Neural Architecture Search: Insights from 1000 Papers","created":"2023-01-20","cited_by_count":139,"area":null},{"id":"2208.04321","title":"Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment","created":"2022-08-08","cited_by_count":17,"area":null},{"id":"2406.12208","title":"Knowledge Fusion By Evolving Weights of Language Models","created":"2024-06-18","cited_by_count":16,"area":null},{"id":"2104.00597","title":"One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking","created":"2021-04-01","cited_by_count":12,"area":null},{"id":"2206.01520","title":"A Survey on Computationally Efficient Neural Architecture Search","created":"2022-06-03","cited_by_count":12,"area":null},{"id":"2108.11014","title":"iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization","created":"2021-08-25","cited_by_count":10,"area":null},{"id":"2110.10165","title":"NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters","created":"2021-10-19","cited_by_count":9,"area":null},{"id":"2203.06905","title":"Less is More: Proxy Datasets in NAS approaches","created":"2022-03-14","cited_by_count":9,"area":null},{"id":"2209.06399","title":"A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends","created":"2022-09-14","cited_by_count":9,"area":null},{"id":"2303.06856","title":"Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies","created":"2023-03-13","cited_by_count":9,"area":null},{"id":"2204.12726","title":"PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search","created":"2022-04-27","cited_by_count":8,"area":null},{"id":"2101.09300","title":"A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks","created":"2021-01-22","cited_by_count":7,"area":null},{"id":"2109.10047","title":"Search For Deep Graph Neural Networks","created":"2021-09-21","cited_by_count":6,"area":null},{"id":"2301.04020","title":"Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence","created":"2022-12-13","cited_by_count":6,"area":null},{"id":"2403.13134","title":"Robust NAS under adversarial training: benchmark, theory, and beyond","created":"2024-03-19","cited_by_count":6,"area":null},{"id":"2103.12529","title":"Enhanced Gradient for Differentiable Architecture Search","created":"2021-03-23","cited_by_count":5,"area":null},{"id":"2105.04885","title":"Graph-based Neural Architecture Search with Operation Embeddings","created":"2021-05-11","cited_by_count":5,"area":null},{"id":"2112.03395","title":"Manas: Mining Software Repositories to Assist AutoML","created":"2021-12-06","cited_by_count":5,"area":null},{"id":"2206.05625","title":"Exploring the Intersection between Neural Architecture Search and Continual Learning","created":"2022-06-11","cited_by_count":4,"area":null},{"id":"2208.08569","title":"ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach","created":"2022-08-17","cited_by_count":4,"area":null},{"id":"2208.13246","title":"AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images","created":"2022-08-28","cited_by_count":4,"area":null},{"id":"2301.06687","title":"DQNAS: Neural Architecture Search using Reinforcement Learning","created":"2023-01-17","cited_by_count":4,"area":null},{"id":"2311.07870","title":"AutoML for Large Capacity Modeling of Meta's Ranking Systems","created":"2023-11-14","cited_by_count":4,"area":null},{"id":"2305.07135","title":"Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems","created":"2023-05-11","cited_by_count":3,"area":null},{"id":"2102.12985","title":"A Novel Framework for Neural Architecture Search in the Hill Climbing Domain","created":"2021-02-22","cited_by_count":2,"area":null},{"id":"2106.14771","title":"HALF: Holistic Auto Machine Learning for FPGAs","created":"2021-06-28","cited_by_count":2,"area":null},{"id":"2107.12917","title":"Experiments on Properties of Hidden Structures of Sparse Neural Networks","created":"2021-07-27","cited_by_count":2,"area":null},{"id":"2205.10456","title":"PSO-Convolutional Neural Networks with Heterogeneous Learning Rate","created":"2022-05-20","cited_by_count":2,"area":null},{"id":"2211.06860","title":"An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecture","created":"2022-11-13","cited_by_count":2,"area":null},{"id":"2305.10014","title":"A Survey on Multi-Objective based Parameter Optimization for Deep Learning","created":"2023-05-17","cited_by_count":2,"area":null},{"id":"2502.06885","title":"Topological derivative approach for deep neural network architecture adaptation","created":"2025-02-08","cited_by_count":2,"area":null},{"id":"2606.29582","title":"Bilevel Optimization for Neural Architecture Search","created":"2026-06-28","cited_by_count":2,"area":null},{"id":"2106.07211","title":"Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures","created":"2021-06-14","cited_by_count":1,"area":null},{"id":"2109.05919","title":"Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability","created":"2021-09-13","cited_by_count":1,"area":null},{"id":"2110.10876","title":"Evolving Transferable Neural Pruning Functions","created":"2021-10-21","cited_by_count":1,"area":null},{"id":"2207.10334","title":"Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling","created":"2022-07-21","cited_by_count":1,"area":null},{"id":"2306.02648","title":"Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search","created":"2023-06-05","cited_by_count":1,"area":null},{"id":"2401.00379","title":"DREAM: Debugging and Repairing AutoML Pipelines","created":"2023-12-31","cited_by_count":1,"area":null},{"id":"2402.14131","title":"Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation","created":"2024-02-21","cited_by_count":1,"area":null},{"id":"2404.08002","title":"ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers","created":"2024-04-08","cited_by_count":1,"area":null},{"id":"2103.03875","title":"Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network","created":"2021-02-26","cited_by_count":0,"area":null},{"id":"2103.08312","title":"Trainless Model Performance Estimation for Neural Architecture Search","created":"2021-03-10","cited_by_count":0,"area":null},{"id":"2106.14776","title":"Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN","created":"2021-06-28","cited_by_count":0,"area":null},{"id":"2110.14369","title":"ConAM: Confidence Attention Module for Convolutional Neural Networks","created":"2021-10-27","cited_by_count":0,"area":null},{"id":"2202.09556","title":"HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks","created":"2022-02-19","cited_by_count":0,"area":null},{"id":"2206.07280","title":"ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions","created":"2022-06-15","cited_by_count":0,"area":null},{"id":"2207.05451","title":"Adversarial Robustness Assessment of NeuroEvolution Approaches","created":"2022-07-12","cited_by_count":0,"area":null},{"id":"2211.10921","title":"MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models","created":"2022-11-20","cited_by_count":0,"area":null},{"id":"2212.03942","title":"An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks","created":"2022-12-07","cited_by_count":0,"area":null},{"id":"2212.06368","title":"Single Cell Training on Architecture Search for Image Denoising","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2305.03416","title":"Evolution under Length Constraints for CNN Architecture design","created":"2023-05-05","cited_by_count":0,"area":null},{"id":"2307.09099","title":"A Survey on Multi-Objective Neural Architecture Search","created":"2023-07-18","cited_by_count":0,"area":null},{"id":"2401.01772","title":"A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure","created":"2024-01-03","cited_by_count":0,"area":null},{"id":"2502.03553","title":"Efficient Global Neural Architecture Search","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2505.17309","title":"Decoupling Representation and Learning in Genetic Programming: the LaSER Approach","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2603.25517","title":"NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2605.19247","title":"Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2606.22991","title":"Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.07637","title":"An optimal control approach for neural network architecture adaptation with a posteriori error estimation","created":"2026-07-08","cited_by_count":0,"area":null}],"next_cursor":null}