Similarity Function

Similarity functions quantify the resemblance between data points, a crucial component in diverse machine learning applications like clustering, classification, and information retrieval. Current research emphasizes developing efficient and accurate similarity functions, particularly focusing on adapting them to handle high-dimensional data, diverse data distributions, and privacy constraints; approaches range from leveraging dimensionality reduction techniques and kernel methods to learning similarity functions directly from data using deep learning architectures like Siamese and contrastive networks. These advancements improve the performance and generalizability of numerous algorithms, impacting fields ranging from music analysis to robotics and differentially private data analysis.

Papers

March 13, 2024

Efficiently Computing Similarities to Private Datasets
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
Density Estimation Differential Privacy SGD Private Datasets Private Model Training Similarity Function

October 27, 2022

Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis
Axel Marmoret, Jérémy E. Cohen, Frédéric Bimbot
Application Proficiency Segmentation Algorithm Music Structure Analysis Block Matching Similarity Function Self Similarity Matrix

August 26, 2022

Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue, Daniele Magazzeni
Raw Data Similarity Learning Similarity Score Efficient Sampling Multiple Distribution Apple Fruitlet Similarity Function

July 26, 2022

Active Learning of Ordinal Embeddings: A User Study on Football Data
Christoffer Loeffler, Kion Fallah, Stefano Fenu, Dario Zanca, Bjoern Eskofier, Christopher John Rozell, Christopher Mutschler
Active Learning Deep Metric Learning User Study Distance Metric Google Research Football Similarity Function Ordinal Latent

April 18, 2022

Learning to Retrieve Relevant Experiences for Motion Planning
Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava, Lydia E. Kavraki
LeArning Abstract Motion Planning eXperience Report Motion Planning Problem Sampling Based Motion Planner Local Representation Similarity Function

January 24, 2022

Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský, Laura Nenzi
Machine Learning Signal Temporal Logic Stochastic Process Kernel Function Kernel Trick Similarity Function

January 7, 2022

Generalized quantum similarity learning
Santosh Kumar Radha, Casey Jao
High Similarity Similarity Measure Similarity Function

November 29, 2021

Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, Jonathan Tompson
Reinforcement Learning Contrastive Learning Zero Shot Strong Generalization Offline Reinforcement Learning Similarity Function