Search Results for author:
Found 8 papers, 5 papers with code
FreeAugment: Data Augmentation Search Across All Degrees of Freedom
Data augmentation has become an integral part of deep learning, as it is known to improve the generalization capabilities of neural networks.
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models.
Diverse Imagenet Models Transfer Better
Encouraged by the recent transferability results of self-supervised models, we propose a method that combines self-supervised and supervised pretraining to generate models with both high diversity and high accuracy, and as a result high transferability.
BINAS: Bilinear Interpretable Neural Architecture Search
Practical use of neural networks often involves requirements on latency, energy and memory among others.
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others.
Ranked #21 on
Neural Architecture Search
on ImageNet
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels
These virtual points, along with the means and variances of their unknown function values estimated using the simple kernel of the first stage, are fitted to a more sophisticated kernel model in the second stage.
XNAS: Neural Architecture Search with Expert Advice
This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.
ASAP: Architecture Search, Anneal and Prune
In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.
