Search Results for author:
Found 7 papers, 6 papers with code
Deep Equilibrium Optical Flow Estimation
Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation.
Ranked #1 on
Optical Flow Estimation
on KITTI 2015 (train)
NAS-Bench-x11 and the Power of Learning Curves
While early research in neural architecture search (NAS) required extreme computational resources, the recent releases of tabular and surrogate benchmarks have greatly increased the speed and reproducibility of NAS research.
A Study on Encodings for Neural Architecture Search
First we formally define architecture encodings and give a theoretical characterization on the scalability of the encodings we study Then we identify the main encoding-dependent subroutines which NAS algorithms employ, running experiments to show which encodings work best with each subroutine for many popular algorithms.
Intra-Processing Methods for Debiasing Neural Networks
Intra-processing methods are designed specifically to debias large models which have been trained on a generic dataset and fine-tuned on a more specific task.
Exploring the Loss Landscape in Neural Architecture Search
In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms.
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is coupled with a neural predictor.
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search
We develop a path-based encoding scheme to featurize the neural architectures that are used to train the neural network model.
