https://github.com/matt-lourens/hierarqcal
공식 코드Hierarchical quantum circuit representations for neural architecture search
Papers with Code 아카이브
Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/matthewpurri/Teaching-Cameras-to-Feel
공식 코드Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
https://github.com/mbilalzafar/fair-classification
공식 코드https://github.com/mechanicalsea/sugar
공식 코드EfficientTDNN: Efficient Architecture Search for Speaker Recognition
https://github.com/megvii-model/DetNAS
공식 코드https://github.com/megvii-model/RLNAS
공식 코드https://github.com/mehravehj/Balanced-Mixture-of-SuperNets
공식 코드Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture
https://github.com/meijieru/AtomNAS
공식 코드https://github.com/menggedu/autoke
공식 코드AutoKE: An automatic knowledge embedding framework for scientific machine learning
https://github.com/mfji/nash
공식 코드NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models
https://github.com/mhnnunes/nas_gnn
공식 코드https://github.com/michailchatzianastasis/graph-based_nas_with_operation_embeddings
공식 코드Graph-based Neural Architecture Search with Operation Embeddings
https://github.com/microsoft/NeuralSpeech
공식 코드LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/microsoft/archai
공식 코드https://github.com/microsoft/automoe
공식 코드https://github.com/microsoft/cream
공식 코드https://github.com/microsoft/petridishnn
공식 코드https://github.com/mikelzc1990/nsganetv2
공식 코드Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
https://github.com/mila-iqia/Awesome-Offline-Model-Based-Optimization
공식 코드https://github.com/milkmilk511/lapt
공식 코드Design Principle Transfer in Neural Architecture Search via Large Language Models
https://github.com/mindspore-ai/models/tree/master/research/cv/ISyNet
공식 코드ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/mingkai-zheng/genius
공식 코드https://github.com/minh1409/dft-npzs-nas
공식 코드https://github.com/miraclehh/nas_privacy
공식 코드https://github.com/mit-han-lab/amc
공식 코드AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/gan-compression
공식 코드GAN Compression: Efficient Architectures for Interactive Conditional GANs
https://github.com/mit-han-lab/hardware-aware-transformers
공식 코드HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/mit-han-lab/once-for-all
공식 코드Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/mit-han-lab/spvnas
공식 코드Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mkhodak/relax
공식 코드https://github.com/mming11/robustdatasetdistillation
공식 코드Group Distributionally Robust Dataset Distillation with Risk Minimization
https://github.com/mo666666/sgm
공식 코드On the Adversarial Transferability of Generalized "Skip Connections"
https://github.com/mohamed-imed-eddine/harmonic-nas
공식 코드Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices
https://github.com/mohsaied/zero-cost-nas
공식 코드https://github.com/moremnas/MoreMNAS
공식 코드Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/msuzen/research/tree/main/gibbs-randomness-compression
공식 코드Gibbs randomness-compression proposition: An efficient deep learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.