Model Optimization

49 papers with code • 0 benchmarks • 0 datasets

To Optimize already existing models in Training/Inferencing tasks.

Most implemented papers

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

allenai/cartography EMNLP 2020

Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.

Dynamic Scale Training for Object Detection

yukang2017/Stitcher 26 Apr 2020

We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.

Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

kjetil-lye/iterative_surrogate_optimization 13 Aug 2020

We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.

Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion

PaddlePaddle/PaddleOCR 21 Feb 2022

By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.

PIXOR: Real-time 3D Object Detection from Point Clouds

DerrickXuNu/OpenCOOD CVPR 2018

Existing approaches are, however, expensive in computation due to high dimensionality of point clouds.

AutoScale: Learning to Scale for Crowd Counting and Localization

dk-liang/AutoScale 20 Dec 2019

A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.

Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

sumonbis/ML-Fairness 21 May 2020

Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance.

Personalized Federated Learning with Moreau Envelopes

CharlieDinh/pFedMe NeurIPS 2020

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.

Multi-objective Asynchronous Successive Halving

mbilalzafar/fair-classification 23 Jun 2021

Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.

Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks

abhaskumarsinha/GRU-varients 20 Jan 2017

The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates.