Study examines conditions for entire bounded or large solutions to elliptic equations under radiality.
problem Conditions for existence of entire bounded or large solutions to elliptic equations.
method Analyzes Laplace operator and Laplace Beltrami operator on harmonic NA groups and Euclidean spaces, with focus on radiality of the equation. result Necessary and sufficient conditions for existence of entire bounded or large solutions are provided.
New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.
The study of flat symplectic Lie algebras and groups.
problem Characterizing and understanding flat symplectic Lie algebras and groups.
method Analyzing the derived ideal, curvature, and double extension process.
result Every flat symplectic Lie algebra is obtained by a sequence of double extensions starting from the trivial algebra.
The Fourier transform on negatively curved harmonic manifolds is studied.
problem Analyzing the Fourier transform on negatively curved harmonic manifolds.
method Defining and proving the Fourier transform and inversion formula for functions on these manifolds.
result A Fourier inversion formula and Plancherel theorem are proven for functions on negatively curved harmonic manifolds.
NAS-Bench-Suite simplifies NAS evaluation across diverse tasks.
problem Limited and inconsistent NAS benchmarks hinder research reproducibility.
method Developed a comprehensive, extensible NAS benchmark suite.
result Many NAS conclusions do not generalize across different benchmarks.
WeakNAS uses a set of weaker predictors to find top architectures with fewer samples.
problem Finding the best neural architecture with heavy computation costs.
method Proposes a paradigm shift from fitting the whole architecture space to progressively fitting a search path through a set of weaker predictors.
result WeakNAS produces coarse-to-fine iteration to gradually refine the ranking of sampling space, requiring fewer samples to find top-performance architectures.
Paper proposes GP-NAS-ensemble for fast neural architecture performance prediction.
problem Estimating neural network performance without training time-consuming evaluations.
method GP-NAS-ensemble framework using ensemble learning improvements.
result Ranked second in a NAS performance prediction challenge.
HM-NAS improves neural architecture search by learning optimal architectures.
problem Limited flexibility in architecture candidates due to hand-designed heuristics.
method Incorporates multi-level encoding and hierarchical masking to automatically learn optimal architectures.
result Achieves better architecture search performance and competitive model accuracy.
BRP-NAS uses GCNs to predict neural network performance for more efficient NAS.
problem Inaccurate performance metrics in NAS lead to suboptimal model designs.
method Proposes BRP-NAS, a hardware-aware NAS using GCNs for accurate performance prediction.
result BRP-NAS outperforms previous methods on NAS-Bench-101 and 201, improving model sample efficiency.
Single-Path NAS designs efficient ConvNets in under 4 hours.
problem Designing efficient ConvNets for mobile devices under time constraints.
method Differentiable NAS method using one single-path ConvNet.
result Achieves 74.96% top-1 accuracy on ImageNet with 79ms latency.
Single-Path NAS reduces NAS search cost to 3 hours, achieving state-of-the-art mobile image classification.
problem Efficiently automate ConvNet design under mobile latency constraints.
method Single-Path NAS, using one single-path ConvNet with shared parameters.
result Achieves state-of-the-art top-1 ImageNet accuracy (75.62%) in 8 epochs (24 TPU-hours).
Single-Path NAS designs efficient ConvNets for mobile devices in hours.
problem Designing efficient ConvNets for mobile devices under latency constraints.
method Single-Path NAS, a differentiable method that reduces search cost and inference time.
result Achieves state-of-the-art accuracy on ImageNet with 79ms inference latency.
Paper connects neural network hyperparameter optimization and NAS to structured sparse recovery.
problem Hyperparameter optimization and neural architecture search in neural networks.
method Structured sparse recovery methods applied to HPO and NAS.
result Improvements in hyperparameter optimization and discovery of novel neural architectures.
NAS best practices guide reduces evaluation issues.
problem Lack of scientific evaluation quality in NAS.
method Described NAS best practices and checklist.
result Reduces evaluation issues in NAS.
A framework benchmarks and dissects one-shot neural architecture search methods.
problem Understanding the dynamics and components of one-shot NAS methods.
method General framework for one-shot NAS, benchmarking with NAS-Bench-101.
result Comparison of one-shot NAS methods and their sensitivity to hyperparameters.
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Random search outperforms leading NAS methods in neural architecture search.
problem Improving the reproducibility and efficiency of neural architecture search.
method Proposed random search with early-stopping and weight-sharing as NAS baselines.
result Random search with weight-sharing achieves state-of-the-art results on PTB and highly competitive results on CIFAR-10.
HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.
problem Finding fitting neural networks that adhere to hard resource constraints.
method Accurate formulation of resource requirement and scalable search method.
result HardCoRe-NAS generates state-of-the-art architectures strictly satisfying hard resource constraints.
Enhanced tabular benchmarks for energy-efficient neural architecture search.
problem Energy consumption in deep learning models.
method Introducing EC-NAS, an enhanced tabular benchmark with energy consumption data.
result EC-NAS reveals a balance between energy usage and accuracy in neural architecture search.
New benchmarks provide full training data for NAS research.
problem Limited training data on popular benchmarks restricts multi-fidelity techniques.
method SVD and noise modeling to create surrogate benchmarks with full training info.
result Learning curve extrapolation framework improves single-fidelity algorithms.
BS-NAS broadens and shrinks search space for optimal neural architectures.
problem Suboptimal channel numbers and model averaging effects in One-Shot NAS methods.
method Broadening with spring block for channel search, shrinking with underperforming operations removal, evolutionary algorithm for optimal architecture search.
result BS-NAS achieves state-of-the-art performance on ImageNet.
Proposes baselines for joint NAS and HPO optimization.
problem Joint optimization of neural architecture and hyperparameters for multiple objectives.
method Extends existing methods to jointly optimize with multiple objectives.
result Serves as simple baselines for future multi-objective joint NAS + HPO research.
This study analyzes NAS benchmarks and finds that only a subset of operations is crucial for generating high-performing architectures.
problem NAS benchmarks lack generability and provide skewed performance distributions, leading to unreliable comparisons.
method Empirical analysis of widely used NAS benchmarks (101, 201, TransNAS-Bench-101) focusing on operation importance and generability.
result Only a subset of operations is necessary to generate architectures close to the upper-bound performance range, and convolution layers have the highest impact.
Study on encoding neural architectures for NAS, showing impact on performance.
problem Impact of different encodings on NAS performance.
method Formal definition and characterization of encodings, empirical study of encodings' effectiveness.
result Different encodings can significantly affect NAS performance.
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.
New p-harmonic and harmonic morphisms found on Lie groups.
problem Constructing explicit p-harmonic and harmonic morphisms on Lie groups.
method Using the method of eigenfamilies to construct explicit complex-valued p-harmonic functions and harmonic morphisms.
result Explicit complex-valued p-harmonic functions and harmonic morphisms constructed on non-compact classical Lie groups.
DSNAS optimizes neural architecture and parameters in one step.
problem Poor correlation between architecture performance in two stages of NAS methods.
method Task-specific end-to-end approach with DSNAS framework.
result DSNAS discovers comparable accuracy networks in less time.
New agent learns from previous search spaces to improve NAS efficiency.
problem NAS requires restarting learning from scratch between different search spaces.
method Transformer-based agent for joint training and knowledge transfer.
result Efficient knowledge transfer between search spaces improves NAS performance.
This work benchmarks and theorizes robust NAS under adversarial training.
problem Lack of benchmark evaluations and theoretical guarantees for robust NAS architectures under adversarial training.
method Released a comprehensive data set and established a generalization theory using the neural tangent kernel.
result Established a generalization theory for robust NAS architectures under adversarial training.
NAS-Bench-101 simplifies neural architecture search by creating a searchable dataset.
problem Difficulty in reproducing NAS experiments due to high computational costs.
method Created a compact, expressive search space with 423k unique architectures, trained and evaluated them on CIFAR-10, and compiled results into a dataset.
result Researchers can evaluate diverse models in milliseconds by querying the pre-computed dataset.
Prune and Replace NAS expands search space for faster network discovery.
problem Limited search space in recent NAS algorithms.
method Progressively prunes and replaces candidates to explore a larger search space.
result Achieves state-of-the-art error rates on CIFAR datasets.
Harmonic maps studied in sub-Riemannian geometry for Lie groups.
problem Defining and characterizing harmonic maps in sub-Riemannian settings.
method Generalization of Riemannian harmonic maps to sub-Riemannian manifolds and Lie groups.
result Conditions for sub-Riemannian harmonic maps and their classification.
Paper shows equivalence between NA and ACLMM in diffusion models.
problem No arbitrage condition and existence of ACLMM in general diffusion models.
method Investigates equivalence between NA and ACLMM in single asset diffusion market models.
result NA is equivalent to ACLMM plus mild conditions on scale function and absence of reflecting boundaries.
NAS-X improves inference and model learning for SLVMs.
problem Challenges in analytic inference and model learning for flexible SLVMs.
method NAS-X combines reweighted wake-sleep and smoothing sequential Monte Carlo.
result NAS-X provides low-bias and low-variance gradient estimates.
Paper proposes an iterative NAS technique inspired by Boolean function learning.
problem Challenges in Neural Architecture Search (NAS).
method Inspired by algorithms for learning low-degree sparse Boolean functions.
result Validated on DARTs and NAS-Bench-201, providing theoretical analysis.
MAML with over-parameterized DNNs converges globally at a linear rate.
problem Few-shot learning with limited data.
method Model-agnostic meta-learning (MAML) with over-parameterized deep neural networks (DNNs).
result MAML with over-parameterized DNNs converges globally at a linear rate.
Hill-climbing is a powerful baseline for NAS, even with reduced noise.
problem Noise in evaluating neural architectures.
method Hill-climbing algorithm, denoising training pipeline.
result Hill-climbing outperforms state-of-the-art NAS algorithms with reduced noise.
Study accelerates NAS research with a large dataset of ZC proxies.
problem Speeding up neural architecture search with ZC proxies.
method Created NAS-Bench-Suite, evaluated 13 ZC proxies across 28 tasks, and provided a unified codebase.
result ZC proxies capture substantial complementary information and can improve NAS algorithm performance.
Scalable NAS by factorizing operators into subspaces.
problem Scaling up NAS search space while avoiding operator competition.
method Factorizing a large set of candidate operators into smaller subspaces.
result Achieved state-of-the-art performance on CIFAR10 and ImageNet.
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.
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.
New financial model revises risk measure under NA condition.
problem Revising classical financial mathematics with coherent risk measure on L0. method Developed a new version of the fundamental theorem of asset pricing and provided dual representations.
result Set of risk-hedging prices is closed under NA condition.
Study finds all 4D Lie groups with harmonic curvature.
problem Finding Lie groups with harmonic curvature in 4D.
method Analyzing pp-wave Lie groups and determining harmonic curvature conditions.
result Description of 4D pp-wave Lie groups obtained.
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.
NAS evaluation is hard due to lack of standard protocols.
problem Difficulty in comparing NAS methods due to varying search spaces and evaluation protocols.
method Benchmarked 8 NAS methods on 5 datasets, proposing a relative improvement metric over random architectures.
result Many NAS techniques struggle to significantly outperform a randomly generated architecture.
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.
Survey of AutoML methods and their performance.
problem Building high-quality DL systems requires human expertise.
method Reviews AutoML methods including data preparation, feature engineering, hyperparameter optimization, and neural architecture search.
result Summarizes the performance of NAS algorithms on CIFAR-10 and ImageNet datasets.
Paper uses CMAB to improve NAS efficiency and accuracy.
problem Improving efficiency and accuracy of NAS for DNNs.
method Formulated NAS as CMAB, used Nested Monte-Carlo Search.
result Discovered cell structure achieves comparable accuracy to state-of-the-art, 20x faster.