A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Calibration of simplified vine copulas using noise contrastive estimation
The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial, and classification. To apply it to real-valued datasets, the ICWS algorithm has become a seminal approach that is widely used, and provides state-of-the-art performance for this problem space. However, ICWS suffers…
Simplified optimization for structured matrices in deep learning.
Simplified approach to pseudo-Anosov flows on 3-manifolds.
Paper unifies and simplifies proof of free product conditions.
Novel approach simplifies VI problems with faster performance.
Paper studies simplified trisections and their equivalence classes.
Unified analysis simplifies Johnson-Lindenstrauss lemma for data reduction.
In this paper, we present a deep neural network (DNN) training approach called the "DeepMimic" training method. Enormous amounts of data are available nowadays for training usage. Yet, only a tiny portion of these data is manually labeled, whereas almost all of the data are unlabeled. The training approach presented ut…
Simplified neural network EFTs reveal a single critical condition.
In this paper we continue our systematic analysis of the operatorial approach previously proposed in an economical context and we discuss a {\em mixed} toy model of a simplified stock market, i.e. a model in which the price of the shares is given as an input. We deduce the time evolution of the portfolio of the various…
The paper simplifies FLRW photon propagators using geometric embeddings.
Deep learning using multi-layer neural networks (NNs) architecture manifests superb power in modern machine learning systems. The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable pe…
Simplified proof for Cheeger's isoperimetric constant.
A simplified trisection is a trisection map on a 4-manifold such that, in its critical value set, there is no double point and cusps only appear in triples on innermost fold circles. We give a necessary and sufficient condition for a 3-tuple of systems of simple closed curves in a surface to be a diagram of a simplifie…
Classifies 3-manifolds from simplified (2,0)-trisections of 4-manifolds.
Hypernetworks are simplified simplicial complexes with curvature.
Simplifies neural network models by explicitly enforcing constraints in Cartesian coordinates.
In this note we sketch an initial tentative approach to funding costs analysis and management for contracts with bilateral counterparty risk in a simplified setting. We depart from the existing literature by analyzing the issue of funding costs and benefits under the assumption that the associated risks cannot be hedge…
A new scheme for FBSDEs simplifies computation without Monte Carlo.
Shapes of four dimensional spaces can be studied effectively via maps to standard surfaces. We explain, and illustrate by quintessential examples, how to simplify such generic maps on 4-manifolds topologically, in order to derive simple decompositions into much better understood manifold pieces. Our methods not only al…
Simplified image clustering achieves competitive results without text-based embeddings.
Simplified construction recovers Todd class using algebraic methods.
Simplifies efficient estimation via automatic differentiation and probabilistic programming.
Simplified proof of Honda-Huang's contact convexity result.
Tensor approach simplifies Euclidean space descriptions.
Study on nonorientable 4-manifolds using simplified fibrations and trisections.
A branched covering surface-knot is a surface-knot in the form of a branched covering over a surface-knot. For a branched covering surface-knot, we have a numerical invariant called the simplifying number. We show that branched covering surface-knots with degree three have the simplifying numbers less than three.
Proves a conjecture about the maximum tet-volume of triangulations of a 2-sphere.
Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enormous complexity. In this study, we present a method to make a complex tree ensemble interpretable by simplifying the model. Specifically, we …
A simplified Bayesian approach for online sports rating.
Simplified argument for second order estimate in quaternionic Calabi-Yau problem.
Residual Neural Networks (ResNets) achieve state-of-the-art performance in many computer vision problems. Compared to plain networks without residual connections (PlnNets), ResNets train faster, generalize better, and suffer less from the so-called degradation problem. We introduce simplified (but still nonlinear) vers…
Unified framework for unsupervised concept extraction simplifies guarantees.
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.
Simplified tutorial on doubly robust learning for causal inference.
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow f…
A time schedule simplifies learning in flow-based models for high-dimensional data.
Many methods for reducing and simplifying differential equations are known. They provide various generalizations of the original symmetry approach of Sophus Lie. Plenty of relations between them have been noticed and in this note a unifying approach will be discussed. It is rather close to the classical differential co…
We give a new algorithm to simplify a given triangulation with respect to a given curve. The simplification uses flips together with powers of Dehn twists in order to complete in polynomial time in the bit-size of the curve.
Simplified DGPs training by fixing inducing inputs to subset of data.
Bayesian approach groups observations with similar effects for better inference.
We compare two different bilateral counterparty valuation adjustment (BVA) formulas. The first formula is an approximation and is based on subtracting the two unilateral Credit Valuation Adjustment (CVA)'s formulas as seen from the two different parties in the transaction. This formula is only a simplified representati…
Simplified non-contrastive learning avoids representation collapse.
We consider a Black-Scholes market in which a number of stocks and an index are traded. The simplified Capital Asset Pricing Model is the conjunction of the usual Capital Asset Pricing Model, or CAPM, and the statement that the appreciation rate of the index is equal to its squared volatility plus the interest rate. (T…
New simulation method simplifies Heston model with Poisson conditioning for better accuracy and efficiency.
The paper challenges the notion that asset return doesn't affect Black-Scholes-Merton model.
We present a graded-geometric approach to modular classes of Lie algebroids and their generalizations, introducing in this setting an idea of relative modular class of a Dirac structure for a certain type of Courant algebroids, called projectable. This novel approach puts several concepts related to Poisson geometry an…