Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
Measurement of text similarity: a survey
J Wang, Y Dong - Information, 2020 - mdpi.com
Text similarity measurement is the basis of natural language processing tasks, which play an
important role in information retrieval, automatic question answering, machine translation …
important role in information retrieval, automatic question answering, machine translation …
Thermodynamic unification of optimal transport: Thermodynamic uncertainty relation, minimum dissipation, and thermodynamic speed limits
Thermodynamics serves as a universal means for studying physical systems from an energy
perspective. In recent years, with the establishment of the field of stochastic and quantum …
perspective. In recent years, with the establishment of the field of stochastic and quantum …
Learning generative models with sinkhorn divergences
The ability to compare two degenerate probability distributions, that is two distributions
supported on low-dimensional manifolds in much higher-dimensional spaces, is a crucial …
supported on low-dimensional manifolds in much higher-dimensional spaces, is a crucial …
Sample complexity of Sinkhorn divergences
Optimal transport (OT) and maximum mean discrepancies (MMD) are now routinely used in
machine learning to compare probability measures. We focus in this paper on Sinkhorn …
machine learning to compare probability measures. We focus in this paper on Sinkhorn …
An introduction to neural information retrieval
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …
rank search results in response to a query. Traditional learning to rank models employ …
Low-resource languages: A review of past work and future challenges
A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …
useful training attributes such as supervised data, number of native speakers or experts, etc …
Sentence mover's similarity: Automatic evaluation for multi-sentence texts
For evaluating machine-generated texts, automatic methods hold the promise of avoiding
collection of human judgments, which can be expensive and time-consuming. The most …
collection of human judgments, which can be expensive and time-consuming. The most …
Optimal transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as
undirected graphs, seen as probability distributions in a specific metric space. We consider a …
undirected graphs, seen as probability distributions in a specific metric space. We consider a …
Neural models for information retrieval
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …
rank search results in response to a query. Traditional learning to rank models employ …