7 citations · 7 across the 4 of their papers we have counts for
5 papers
Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova
In many machine learning applications, the most relevant items for a query should be efficiently retrieved. The relevance function is usually an expensive similarity model, making…
Cluster Attention for Graph Machine Learning
Oleg Platonov, Liudmila Prokhorenkova
Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message p…
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Andrey Malinin, Neil Band, Ganshin +17
There has been significant research done on developing methods for improving robustness to distributional shift and uncertainty estimation. In contrast, only limited work has exami…
Embedding Words in Non-Vector Space with Unsupervised Graph Learning
Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova +1
It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe). While this approach is convenient, it is unnatural for language: words form a…
Overlapping Spaces for Compact Graph Representations
Kirill Shevkunov, Liudmila Prokhorenkova
Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spac…