Search Results for author:
Found 7 papers, 5 papers with code
Gemma 2: Improving Open Language Models at a Practical Size
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters.
COMISR: Compression-Informed Video Super-Resolution
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
Efficient Scale-Permuted Backbone with Learned Resource Distribution
Furthermore, SpineNet is built with a uniform resource distribution over operations.
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.
Ranked #30 on
Neural Architecture Search
on ImageNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Ranked #14 on
Image Classification
on iNaturalist
Scaling Up Neural Architecture Search with Big Single-Stage Models
In this work, we propose BigNAS, an approach that simplifies this workflow and scales up neural architecture search to target a wide range of model sizes simultaneously.
Pooling Pyramid Network for Object Detection
We share box predictors across all scales, and replace convolution between scales with max pooling.
