Constructing interpretable quadratic accuracy predictors to serve as an objective function for an IQCQP problem that represents NAS under latency constraints and solve it with efficient algorithms.

Related tags

Deep LearningIQNAS
Overview

IQNAS: Interpretable Integer Quadratic programming Neural Architecture Search

Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS). However, previous methods use complicated predictors for the accuracy of the network. Those predictors are hard to interpret and sensitive to many hyperparameters to be tuned, hence, the resulting accuracy of the generated models is often harmed. In this work we resolve this by introducing Interpretable Integer Quadratic programming Neural Architecture Search (IQNAS), that is based on an accurate and simple quadratic formulation of both the accuracy predictor and the expected resource requirement, together with a scalable search method with theoretical guarantees. The simplicity of our proposed predictor together with the intuitive way it is constructed bring interpretability through many insights about the contribution of different design choices. For example, we find that in the examined search space, adding depth and width is more effective at deeper stages of the network and at the beginning of each resolution stage. Our experiments show that IQNAS generates comparable to or better architectures than other state-of-the-art NAS methods within a reduced search cost for each additional generated network, while strictly satisfying the resource constraints.

[WIP] The code is being prepared for a public release.

Watch this project and stay tuned.

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

    VarCLR: Variable Representation Pre-training via Contrastive Learning New: Paper accepted by ICSE 2022. Preprint at arXiv! This repository contain

squaresLab 32 Oct 24, 2022
simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

Ramón Casero 1 Jan 07, 2022
Share a benchmark that can easily apply reinforcement learning in Job-shop-scheduling

Gymjsp Gymjsp is an open source Python library, which uses the OpenAI Gym interface for easily instantiating and interacting with RL environments, and

134 Dec 08, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
Diverse Image Generation via Self-Conditioned GANs

Diverse Image Generation via Self-Conditioned GANs Project | Paper Diverse Image Generation via Self-Conditioned GANs Steven Liu, Tongzhou Wang, David

Steven Liu 147 Dec 03, 2022
DROPO: Sim-to-Real Transfer with Offline Domain Randomization

DROPO: Sim-to-Real Transfer with Offline Domain Randomization Gabriele Tiboni, Karol Arndt, Ville Kyrki. This repository contains the code for the pap

Gabriele Tiboni 8 Dec 19, 2022
[ICCV21] Self-Calibrating Neural Radiance Fields

Self-Calibrating Neural Radiance Fields, ICCV, 2021 Project Page | Paper | Video Author Information Yoonwoo Jeong [Google Scholar] Seokjun Ahn [Google

381 Dec 30, 2022
PyTorch implementation for 3D human pose estimation

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach This repository is the PyTorch implementation for the network presented in:

Xingyi Zhou 579 Dec 22, 2022
NeROIC: Neural Object Capture and Rendering from Online Image Collections

NeROIC: Neural Object Capture and Rendering from Online Image Collections This repository is for the source code for the paper NeROIC: Neural Object C

Snap Research 647 Dec 27, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
Automatic labeling, conversion of different data set formats, sample size statistics, model cascade

Simple Gadget Collection for Object Detection Tasks Automatic image annotation Conversion between different annotation formats Obtain statistical info

llt 4 Aug 24, 2022
“Data Augmentation for Cross-Domain Named Entity Recognition” (EMNLP 2021)

Data Augmentation for Cross-Domain Named Entity Recognition Authors: Shuguang Chen, Gustavo Aguilar, Leonardo Neves and Thamar Solorio This repository

<a href=[email protected]"> 18 Sep 10, 2022
Contrastive Learning of Structured World Models

Contrastive Learning of Structured World Models This repository contains the official PyTorch implementation of: Contrastive Learning of Structured Wo

Thomas Kipf 371 Jan 06, 2023
Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021) DBB is a powerful ConvNet building block to replace regul

253 Dec 24, 2022
Multi-Content GAN for Few-Shot Font Style Transfer at CVPR 2018

MC-GAN in PyTorch This is the implementation of the Multi-Content GAN for Few-Shot Font Style Transfer. The code was written by Samaneh Azadi. If you

Samaneh Azadi 422 Dec 04, 2022
Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

SML (ICCV 2021, Oral) : Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Standardi

SangHun 61 Dec 27, 2022
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition

Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition The official code of ABINet (CVPR 2021, Oral).

334 Dec 31, 2022
Tensorflow-Project-Template - A best practice for tensorflow project template architecture.

Tensorflow Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributi

Mahmoud G. Salem 3.6k Dec 22, 2022
image scene graph generation benchmark

Scene Graph Benchmark in PyTorch 1.7 This project is based on maskrcnn-benchmark Highlights Upgrad to pytorch 1.7 Multi-GPU training and inference Bat

Microsoft 303 Dec 27, 2022
This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 2020

Classifier-Balancing This repository contains code for the paper: Decoupling Representation and Classifier for Long-Tailed Recognition Bingyi Kang, Sa

Facebook Research 820 Dec 26, 2022