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Search results for “t-SNE”

64 results

IMO 2002

2026Problem sheet

International mathematics competition for pre-university students, held annually since 1959. Six problems over two days.
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280

IMO 2004

2026Problem sheet

International mathematics competition for pre-university students, held annually since 1959. Six problems over two days.
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240

IMO 2006

2026Problem sheet

International mathematics competition for pre-university students, held annually since 1959. Six problems over two days.
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320

Geometry. Lecture 2

2026LectureФёдор Попеленский

Finite geometries — the Platonic solids. A reminder — right actions, orbits, stabilisers, fundamental domains (corrected definition) and the example of Z+Z\mathbb{Z} + \mathbb{Z} on the plane; the formula ∣G∣=∣Orb⁡(x)∣⋅∣Stab⁡(x)∣|G| = |\operatorname{Orb}(x)| \cdot |\operatorname{Stab}(x)| and the 48 symmetries of the cube; regular polyhedra — Sosinsky's definition and the definition via flags, a stellated counterexample; the five Platonic solids; regular polytopes in R4\mathbb{R}^4 (six) and in Rn\mathbb{R}^n for n≥5n \ge 5 (the cube, the simplex, the hyperoctahedron) with the coordinates of their vertices; why there are no more than five — face angles and the Schläfli symbol; uniqueness of the gluing and the rigidity theorem; Euler's formula; the finite subgroups of SO(3)SO(3) — Zn\mathbb{Z}_n, DnD_n and the rotation groups of the tetrahedron, cube and icosahedron; the duality cube–octahedron, icosahedron–dodecahedron; which figures on the sphere realise Zn\mathbb{Z}_n, DnD_n and Z2\mathbb{Z}_2.

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380

Topology 2. Problem sheet 1

2026Problem sheetФёдор Вылегжанин

Problems for the course "Topology 2" (F. E. Vylegzhanin, Independent University of Moscow, autumn 2026), sheet 1 of 9 September 2026 — simplicial homology and chain complexes. Zeroth homology and connected components; homology of a disjoint union and of a wedge; triangulations of the circle, the sphere and the Möbius band; the join and the product of simplicial complexes; the Euler characteristic and the Morse inequalities; the number of vertices of a triangulated surface; operations on chain complexes; exact sequences; the Smith normal form and the splitting of a complex.
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450

Topology 2. Problem sheet 2

2026Problem sheetФёдор Вылегжанин

Problems for the course "Topology 2" (F. E. Vylegzhanin, Independent University of Moscow, autumn 2026), sheet 2 of 16 September 2026 — singular homology and chain homotopies. A chain map from simplicial to singular chains; retractions and direct summands; the Hurewicz homomorphism from the fundamental group to the first homology; finite CW complexes and homology classes; homology of a union of a chain of spaces; homology of a simplex and of its skeleton; simplicial maps; the chain homotopy equivalence between a complex and its homology; chain homotopy as an equivalence relation; the five lemma.
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400

Vector calculus. Stokes' theorem. Circulation

2026Problem sheetSergey

Circulation of a field as the flux of its curl: computing the curl, matching the orientation of the contour and the normal, circulation computed two ways.
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310

Аффинные пространства

2026LectureTaras Panov

Аффинные пространства и подпространства, аффинные системы координат и формулы их замены, прямые и плоскости в аффинных пространствах.
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460

The torsion component of the Picard scheme

2026OtherBogdan Zavyalov

Every finite flat commutative group scheme over a noetherian local ring is the torsion component of the Picard scheme of a smooth projective scheme with 3-dimensional fibers, built as a quotient of a complete intersection. An application: Hodge numbers that jump in a smooth projective family.
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500

Some examples of algebraic groups

2026TheorySean Cotner

Two pathological phenomena for algebraic groups over general bases — a group degenerating between the multiplicative and additive group across a DVR, and a non-affine identity component — built as centralizers in SL_n.

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180

Machine Learning 2. Lecture 2. Neural Networks for Unsupervised Learning

2026LectureКонстантин Вячеславович Воронцов

Kohonen networks: clustering as a two-layer neural network, the hard (WTA) and soft (WTM) competition rules, self-organising Kohonen maps and their interpretation; autoencoders: the formulation of the problem, the linear autoencoder and the principal component method, sparse, denoising, contractive, relational and variational autoencoders, reparametrisation, autoencoders for supervised learning, multilayer autoencoders; joint training of several models: pre-training and transfer learning, multi-task learning and few-shot learning, self-supervised learning, distillation and surrogate modelling, learning using privileged information (LUPI), generative adversarial networks (GAN).
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210

Machine Learning 2. Lecture 6. Learning to Rank

2026LectureКонстантин Вячеславович Воронцов

The learning-to-rank problem: recovering a partial order relation, the pointwise, pairwise and listwise approaches, the linear ranking model, applications; ordinal regression and the ordinal SVM (SVOR); the pairwise criterion, Ranking SVM, RankNet, the transition to the listwise method LambdaRank; ranking search results: query–document objects, assessor ratings, text, link and click features, TF-IDF and its probabilistic meaning, Okapi BM25, PageRank; quality criteria: precision and recall, MAP, the share of defective pairs and its link with AUC, DCG and NDCG, the pFound user behaviour model; the DSSM neural model: a siamese network, word hashing by letter trigrams, training on clicks with negative sampling.
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130

Machine Learning 2. Lecture 11. Active Learning

2026LectureКонстантин Вячеславович Воронцов

Active learning: the model itself chooses which objects to give the teacher for labelling. The problem setting, why active learning is faster than passive learning, learning curves; pool-based sampling, query synthesis and stream-based selective sampling, cost-sensitive methods; uncertainty sampling (least confidence, minimum margin, maximum entropy), query by committee, version space reduction, expected model change, expected error reduction; query synthesis by the Nelder–Mead method and by the variance reduction criterion; density weighting; exploratory actions, the ε-active and EG-active algorithms with the exponentiated gradient; active learning in crowdsourcing: reconciling annotators' labels, models of their reliability and of object difficulty, assigning tasks to annotators.
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450

Machine Learning. Additional Lecture 2. A Survey of Optimisation Problems in Machine Learning

2026LectureКонстантин Вячеславович Воронцов

A survey lecture: machine learning as formulations of optimisation problems, a model, a loss function, a regulariser. Regression and classification, continuous approximations of the threshold loss function, SVM and logistic regression, quantile, robust and SVM regression; the L0, L1, L2, Elastic Net, SFM, RFM regularisers and feature selection; learning to rank: ordinal regression, the pairwise approach and AUC, LambdaRank; estimating a density and a mixture of distributions, clustering as a special case of a mixture, the one-class SVM; semi-supervised learning, the transductive SVM, PU-learning; matrix factorisations, autoencoders, multidimensional scaling, graph factorisations, GraphEDM; transfer learning, multi-task and self-supervised learning, distillation, learning using privileged information, generative adversarial networks; a typology of machine learning problems.
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210