7 papers
Ultra Fast Warm Start Solution for Graph Recommendations
Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely imp…
Optimization on the Extended Tensor-Train Manifold with Shared Factors
Alexander Molozhavenko, Maxim Rakhuba
This paper studies tensors that admit decomposition in the Extended Tensor Train (ETT) format, with a key focus on the case where some decomposition factors are constrained to be e…
Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations
Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov
Recent studies have demonstrated the potential of hyperbolic geometry for capturing complex patterns from interaction data in recommender systems. In this work, we introduce a nove…
On the Upper Bounds for the Matrix Spectral Norm
Alexey Naumov, Maxim Rakhuba, Denis Ryapolov +1
We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the n…
ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations
Ekaterina Grishina, Mikhail Gorbunov, Maxim Rakhuba
Large language models (LLMs) demonstrate impressive results in natural language processing tasks but require a significant amount of computational and memory resources. Structured…
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov
In this paper, we propose a new geometric approach for knowledge graph completion via low rank tensor approximation. We augment a pretrained and well-established Euclidean model ba…