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Found 10 papers, 4 papers with code
Building Optimal Neural Architectures using Interpretable Knowledge
Neural Architecture Search is a costly practice.
A General-Purpose Transferable Predictor for Neural Architecture Search
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
A Transferable General-Purpose Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications.
L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.
Generative Adversarial Neural Architecture Search
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess.
Generative Adversarial Neural Architecture Search with Importance Sampling
Despite the empirical success of neural architecture search (NAS) algorithms in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to be assessed.
Matching Natural Language Sentences with Hierarchical Sentence Factorization
The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies.
