Search Results for author:
Found 6 papers, 3 papers with code
Multi-objective Differentiable Neural Architecture Search
Pareto front profiling in multi-objective optimization (MOO), i. e. finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives like neural network training.
Weight-Entanglement Meets Gradient-Based Neural Architecture Search
%Due to the inherent differences in the structure of these search spaces, these Since weight-entanglement poses compatibility challenges for gradient-based NAS methods, these two paradigms have largely developed independently in parallel sub-communities.
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2.
Generative Flows with Invertible Attentions
The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationally efficient model.
Neural Architecture Search of SPD Manifold Networks
To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design.
