4 citations · 8 across the 10 of their papers we have counts for
10 papers
Simplex Deep Linear Discriminant Analysis
Maxat Tezekbayev, Arman Bolatov, Zhenisbek Assylbekov
We revisit Deep Linear Discriminant Analysis (Deep LDA) from a likelihood-based perspective. While classical LDA is a simple Gaussian model with linear decision boundaries, attachi…
Deep Linear Discriminant Analysis Revisited
Maxat Tezekbayev, Rustem Takhanov, Arman Bolatov +1
We show that for unconstrained Deep Linear Discriminant Analysis (LDA) classifiers, maximum-likelihood training admits pathological solutions in which class means drift together, c…
Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications
Arman Bolatov, Alan Legg, Igor Melnykov +3
This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data dis…
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods
Rustem Takhanov, Maxat Tezekbayev, Artur Pak +3
The discrete logarithm problem is a fundamental challenge in number theory with significant implications for cryptographic protocols. In this paper, we investigate the limitations…
Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmetic
Rustem Takhanov, Maxat Tezekbayev, Artur Pak +2
Classes of target functions containing a large number of approximately orthogonal elements are known to be hard to learn by the Statistical Query algorithms. Recently this classica…
Long-Tail Theory under Gaussian Mixtures
Arman Bolatov, Maxat Tezekbayev, Igor Melnykov +3
We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020). We demonstrate that a linear classifier cannot decrease the gen…
Autoencoders for a manifold learning problem with a Jacobian rank constraint
Rustem Takhanov, Y. Sultan Abylkairov, Maxat Tezekbayev
We formulate the manifold learning problem as the problem of finding an operator that maps any point to a close neighbor that lies on a ``hidden'' -dimensional manifold. We call…
Speeding Up Entmax
Maxat Tezekbayev, Vassilina Nikoulina, Matthias Gallé +1
Softmax is the de facto standard in modern neural networks for language processing when it comes to normalizing logits. However, by producing a dense probability distribution each…
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics
Vassilina Nikoulina, Maxat Tezekbayev, Nuradil Kozhakhmet +3
There is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether lin…
Semantics- and Syntax-related Subvectors in the Skip-gram Embeddings
Maxat Tezekbayev, Zhenisbek Assylbekov, Rustem Takhanov
We show that the skip-gram embedding of any word can be decomposed into two subvectors which roughly correspond to semantic and syntactic roles of the word.