Complexity Reduction

Complexity reduction in various computational domains, such as machine learning and video coding, aims to improve efficiency without sacrificing performance. Current research focuses on developing techniques like feature selection (e.g., minimum description features), structured pruning of neural networks (e.g., using locality-sensitive hashing), and efficient algorithms for specific operations (e.g., skipping zeros in convolutional layers). These advancements are crucial for deploying computationally intensive models on resource-constrained devices and improving the scalability of large-scale applications, impacting fields ranging from wireless positioning to natural language processing.

Papers

November 9, 2024

Parallel Multi-path Feed Forward Neural Networks (PMFFNN) for Long Columnar Datasets: A Novel Approach to Complexity Reduction
Ayoub Jadouli, Chaker El Amrani
Neural Network Convolutional Neural Network Novel Approach Complexity Reduction 1D Convolutional

August 8, 2024

Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming
Thomas Helmuth, Edward Pantridge, James Gunder Frazier, Lee Spector
Symbolic Regression Genetic Programming Program Synthesis Training Example Complexity Reduction

April 21, 2024

Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
Deep Learning Localization Network Complexity Reduction

February 14, 2024

Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
Deep Learning New Machine Localization Network Self Positioning Complexity Reduction

September 29, 2023

Instant Complexity Reduction in CNNs using Locality-Sensitive Hashing
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
Convolutional Neural Network Locality Sensitive Hashing Vision Benchmark Fast Convolution Complexity Reduction

June 28, 2023

Reduce Computational Complexity for Convolutional Layers by Skipping Zeros
Zhiyi Zhang, Pengfei Zhang, Zhuopin Xu, Qi Wang
Convolutional Neural Network Direct Convolution Convolutional Layer Better Zero Dilated Convolution Complexity Reduction

February 16, 2023

Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction
Jingxin Li, Toktam Mahmoodi, Hak-Keung Lam
LeArning Abstract Heterogeneous Data Global Mixing Heterogeneous Environment Training Instability Complexity Reduction Adaptive Aggregation Asynchronous Update

October 18, 2022

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning
Shuo Xie, Jiahao Qiu, Ankita Pasad, Li Du, Qing Qu, Hongyuan Mei
Transfer Learning Pretrained Language Model Task Specific Hidden State Complexity Reduction Class Variability Task Selection Class Variance

July 29, 2022

Computational complexity reduction of deep neural networks
Mee Seong Im, Venkat R. Dasari
Deep Neural Network Computer Vision DNN Framework Complexity Reduction

June 18, 2022

Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques
Rajeev Patwari, Varo Ly
General Analysis Depth Estimation Monocular Depth Estimation Monocular Vision Stereo Depth Estimation Complexity Reduction

June 10, 2022

Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction
Ngoc Duy Pham, Alsharif Abuadbba, Yansong Gao, Tran Khoa Phan, Naveen Chilamkurti
Differential Privacy Split Learning PRIvacy Leakage Data Privacy Complexity Reduction

May 8, 2022

SSIM-Variation-Based Complexity Optimization for Versatile Video Coding
Jielian Lin, Hongbin Lin, Zhichen Zhang, Yiwen Xu, Tiesong Zhao
Versatile Video Coding High Efficiency Video Coding Structural Similarity Index Complexity Reduction

April 25, 2022

On-demand compute reduction with stochastic wav2vec 2.0
Apoorv Vyas, Wei-Ning Hsu, Michael Auli, Alexei Baevski
Pre Trained Model Pre Training Linear Compression Stochastic Way Transformer Encoder Complexity Reduction Sign Based Compression

March 16, 2022

Complexity Reduction of Learned In-Loop Filtering in Video Coding
Woody Bayliss, Luka Murn, Ebroul Izquierdo, Qianni Zhang, Marta Mrak
Convolutional Neural Network Video Coding Magnitude Based Pruning Complexity Reduction Loop Filtering

Complexity Reduction

Papers

Parallel Multi-path Feed Forward Neural Networks (PMFFNN) for Long Columnar Datasets: A Novel Approach to Complexity Reduction

Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming

Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning

Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features

Instant Complexity Reduction in CNNs using Locality-Sensitive Hashing

Reduce Computational Complexity for Convolutional Layers by Skipping Zeros

Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning

Computational complexity reduction of deep neural networks

Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques

Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction

SSIM-Variation-Based Complexity Optimization for Versatile Video Coding

On-demand compute reduction with stochastic wav2vec 2.0

Complexity Reduction of Learned In-Loop Filtering in Video Coding