Automatically designs CNN architectures for medical image segmentation.
problem Manual design of deep network architectures is time-consuming and resource-intensive.
method Policy gradient reinforcement learning with dice index reward function.
result Efficacy demonstrated with low computational cost compared to state-of-the-art networks.
Paper proposes NASAIC framework for co-designing neural architectures and heterogeneous ASICs.
problem Designing efficient neural architectures and ASICs for multiple tasks.
method Build ASIC templates and propose NASAIC framework for simultaneous design of architectures and ASICs.
result NASAIC ensures design specifications and maximizes accuracy with minimal performance loss.
AGAN automates GAN design, outperforming human-designed models.
problem Designing effective GAN architectures requires human expertise and trial-and-error.
method Automated neural architecture search (AGAN) for deep generative models.
result AGAN finds architectures that outperform state-of-the-art models in unsupervised and supervised image generation tasks.
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.
Survey of 1000 NAS papers, automating neural architecture design.
problem Designing high-performing neural architectures for various tasks.
method Taxonomy of search spaces, algorithms, and speedup techniques.
result NAS has surpassed human-designed architectures on many tasks.
New neural network architecture for auction design exploiting permutation symmetry.
problem Designing incentive-compatible auctions that maximize expected revenue.
method Constructed a permutation-equivariant neural network architecture.
result Permutation-equivariant architectures can perfectly recover optimal mechanisms.
GraphNAS uses reinforcement learning to automatically design graph neural network architectures.
problem Designing effective graph neural network architectures requires manual work and domain knowledge.
method GraphNAS generates variable-length strings to describe architectures and trains a recurrent network with reinforcement learning to maximize validation accuracy.
result GraphNAS achieves consistently better performance on various citation and protein networks.
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.
A new NAS framework optimizes 3D medical image segmentation architectures.
problem Optimizing neural architectures for high-resolution 3D medical images.
method Stochastic sampling algorithm for scalable gradient-based optimization of neural connectivities and operation types in both encoder and decoder.
result Automatically designed architecture outperforms human-designed U-Net.
Survey tackles challenges in neural architecture design.
problem Challenges in designing optimal neural architectures.
method New classification perspective based on early NAS algorithms' characteristics, problems, and solutions.
result Comprehensive analysis and comparison of NAS works.
GP-CNAS uses genetic programming to automatically design CNN architectures.
problem Designing optimal CNN architectures is laborious and error-prone.
method GP-CNAS uses a tree-based representation of CNNs and dynamic crossover operators to search for optimal architectures.
result GP-CNAS finds optimal CNN architectures with balanced depth and width in limited trials.
A new framework generates large hierarchical search spaces for neural architectures.
problem Discovering neural architectures from simple blocks is hard.
method Context-free grammars for a unified, scalable search space.
result Efficiently searches over complete architectures, outperforming existing methods.
Method improves developability of B-spline surfaces for architectural design.
problem Developability of B-spline surfaces for architectural freeform surfaces.
method Algorithm to thin the Gauss image by increasing planarity of neighborhoods, allowing for paneling with specific developable types.
result Enforced developable panels for architectural design, reducing manufacturing costs.
Unified framework for U-Net design and analysis.
problem Understudied design and architecture of U-Nets.
method Theoretical results, Multi-ResNets, function constraints encoding.
result Competitive and superior performance in various tasks.
Holographic Invariant Storage uses vector architectures to ensure LLM safety at design time.
problem Mitigating context drift in large language models (LLMs) during deployment.
method Introduces Holographic Invariant Storage (HIS) protocol that combines known properties of bipolar Vector Symbolic Architectures into a design-time safety contract.
result Closed-form guarantees for single-signal recovery fidelity, continuous-noise robustness, and multi-signal capacity degradation are provided and validated.
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.
NAS helps find best neural network designs.
problem Designing optimal neural network architectures.
method Optimization algorithms and search spaces.
result Introduction to major advances in NAS for CNNs.
We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a su…
Graph-based NAS improves sample efficiency in architecture design.
problem Current NAS search spaces are static sequences, limiting expressiveness.
method Proposed graph-based search space with vertices and edges for iterative and branching decisions.
result Graph representation improves sample efficiency in architecture design.
This thesis aims to automate deep neural network design for efficiency and complexity reduction.
problem Manual design of deep neural networks is inefficient and complex.
method Examines and proposes automated approaches to neural network design.
result Creation of less complex models with good performance through automation.
A ranking network improves neural architecture search efficiency.
problem Efficiently search for neural network architectures without extensive training.
method Pairwise ranking loss for a performance predictor trained on task meta-features.
result The ranking network outperforms the performance predictor in architecture search.
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices…
New NN design for nonlinear systems control with guarantees.
problem Designing NN architectures for nonlinear system control with guarantees.
method Exploits system model to design NN architecture, uses TLL NN for approximation.
result Guaranteed NN architecture sufficient for implementing a controller.
SAEP prunes sub-architectures to reduce search cost while maintaining performance.
problem Redundancy in ensemble sub-architectures leads to high computational cost.
method SAEP leverages diversity to prune sub-architectures, reducing ensemble size.
result SAEP reduces the number of sub-architectures without degrading performance.
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 crypto-oriented neural architectures for faster secure inference.
problem Privacy conflicts between model users and providers in neural network applications.
method Proposes a novel Partial Activation layer to optimize the initial design of crypto-oriented neural architectures.
result Significant improvement in the efficiency of secure inference on common evaluation metrics.
DeepPeep attacks DNN architectures to reveal design details, posing IP theft risks.
problem Protecting DNN architecture from IP theft in cloud-based services.
method Two-stage attack methodology exploiting design characteristics.
result DeepPeep successfully reverses-engineers compact DNN architectures.
The paper introduces capacity allocation analysis for neural networks, focusing on spatial capacity.
problem Designing neural network architectures is challenging due to the interplay of intuition, experimentation, and luck.
method Introduces capacity allocation analysis, focusing on spatial capacity allocation in linear settings.
result Quantitative comparison of classical architectures on various synthetic tasks reveals insights into model capacity allocation.
RandomNet uses random search to design neural architectures without much human intervention.
problem Designing neural architectures without excessive human intervention.
method Random search strategy for multimodal neural architecture design.
result RandomNet performs close to state-of-the-art on AV-MNIST with minimal human supervision.
Automates GNN design for molecular property prediction.
problem Designing and tuning GNN architectures for molecular property prediction is labor-intensive.
method Developed a NAS approach to automatically discover high-performing GNN architectures for MPNNs.
result Automatically discovered MPNNs outperform manually designed GNNs in molecular property prediction.
Proposes a method to automatically design neural networks efficiently.
problem Efficiency in discovering powerful neural network architectures.
method Continuous optimization approach with encoder, predictor, and decoder.
result Competitive performance on image classification and language modeling tasks.
New approach designs optimal structures using GANs and CNNs.
problem Topology design optimization requires many iterations and is impractical for real-world applications.
method Integrates Generative Adversarial Networks (GANs) and convolutional neural networks for topology design.
result Optimal structures generated effectively and rapidly.
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.
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.
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…
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.
FedNAS automates federated learning by searching for better architectures.
problem Non-I.I.D. data makes predefined model architectures suboptimal.
method Federated Neural Architecture Search (FedNAS) for collaborative architecture optimization.
result FedNAS searches for better architectures that outperform predefined models.
Capsule Networks improve clothing retrieval without landmark info.
problem In-shop clothing retrieval performance improvement.
method Triplet-based Capsule Network architecture with SC and RC blocks.
result Triplet Capsule Networks outperform FashionNet and SOTA architectures.
This study optimizes quantized neural networks by considering model architecture and quantization types.
problem Optimizing quantized neural networks for low-power, high-throughput applications.
method Holistic approach including training methods and quantization-friendly architecture design.
result Deeper models are more sensitive to activation quantization, while wider models improve resilience to both weight and activation quantization.
Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1)the ne…
Proposes deep learning methods for handling random vectors.
problem Handling flexible input data like probability measures in deep learning.
method Develops deep architectures to handle permutation invariances, varying weights, and cardinality.
result Demonstrates the effectiveness of deep architectures on measures for classification, reduction, and prediction.
NAT optimizes neural architectures to improve performance without extra cost.
problem Redundant operations in neural architectures consume memory and degrade performance.
method Transformed Markov Decision Process (MDP) and reinforcement learning to replace redundant operations with more efficient ones.
result Transformed architectures outperform original and existing methods on CIFAR-10 and ImageNet datasets.
Graph HyperNetworks (GHN) speed up neural architecture search.
problem Expensive neural architecture search (NAS) requiring training thousands of networks.
method GHN models architecture topology and generates weights via graph neural network.
result GHNs can search nearly 10 times faster than other methods on CIFAR-10 and ImageNet.
New meta-learning method outperforms human-designed architectures in dense image prediction tasks.
problem Designing efficient neural network architectures for dense image prediction.
method Recursive search space construction for multi-scale visual information.
result Meta-learning method achieves state-of-the-art performance on scene parsing, person-part segmentation, and semantic image segmentation.
A new design methodology for neural networks that is guided by traditional algorithm design is presented. To prove our point, we present two heuristics and demonstrate an algorithmic technique for incorporating additional weights in their signal-flow graphs. We show that with training the performance of these networks …
Deep learning predicts patient trajectories in open Mimic-III dataset.
problem Predicting future medical conditions from patient history.
method Two parallel bi-directional Minimal Gated Recurrent Unit networks trained on Mimic-III dataset.
result Significant improvements in automated medical prognosis measured by Recall@k.
Improved neural architecture optimization for energy efficiency.
problem Designing energy-efficient deep learning networks for mobile and edge devices.
method Incorporates energy cost in splitting process and uses a scalable stochastic gradient algorithm to speed up the splitting.
result Trains highly accurate and energy-efficient networks on challenging datasets like ImageNet.
Novel neural architecture improves Bayesian experimental design efficiency.
problem Intractable evaluation of expected information gain (EIG) in Bayesian optimal experimental design.
method Develops a neural architecture that optimizes a single variational model for estimating EIG across many designs, using a lower bound for computational efficiency.
result Significantly improves accuracy in Bayesian experimental design with better sample efficiency.