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arXiv research

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.

168,695 papers · 148 categories

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48 results for Einstein summation

The study shows how certain ODEs and integrals are regular under Borel summation.

problem Analyzing the regularity of solutions to ODEs and integration problems.
method Using geometric perspective on Laplace and Borel transforms, the study examines level 1 ODEs and exponential period integrals over Lefschetz thimbles.
result Solutions of certain ODEs and integration problems are Borel regular.

In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for nn-party real summation in the shuffle model of differential privacy with Oε,δ(1)O_{ε, δ}(1) error and Θ(εn)Θ(ε\sqrt{n}) one-bit messages per party. In contrast, every local model protocol for real summation must incur error Ω(1/n)Ω(1/\sqrt{n}), and there exist …

2019-06-20abs ↗pdf ↗

This paper simplifies computing higher-order UU-statistics efficiently.

problem The inefficiency of computing higher-order UU-statistics in practice.
method Decomposition, connection to Einstein summation, and treewidth-based complexity estimate.
result A new, more efficient algorithm to compute UU-statistics.

Mathematical structures link Gromov-Witten to Donaldson-Thomas invariants.

problem Understanding non-perturbative topological string theory.
method Borel summation of Gromov-Witten potential and analysis of Stokes phenomena.
result Stokes phenomena encode Donaldson-Thomas invariants of the resolved conifold.

Improved greedy 2-coordinate updates for optimization problems with constraints.

problem Minimizing smooth functions subject to constraints.
method Exploiting a connection to steepest descent in the 1-norm, we give faster convergence rates and efficient computation.
result Greedy selection converges faster than random selection and can be computed in O(nlogn)O(n \log n) time.

Stochastic gradient descent updates parameters with summation gradient computed from a random data batch. This summation will lead to unbalanced training process if the data we obtained is unbalanced. To address this issue, this paper takes the error variance and error mean both into consideration. The adaptively adjus…

2018-11-20abs ↗pdf ↗

Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achieve asymptotic estimation optimality for any input distribution, cross-validating for an optimal parameter requires significant computation do…

2011-02-14abs ↗pdf ↗

We provide faster algorithms for the problem of Gaussian summation, which occurs in many machine learning methods. We develop two new extensions - an O(Dp) Taylor expansion for the Gaussian kernel with rigorous error bounds and a new error control scheme integrating any arbitrary approximation method - within the best …

2012-06-27abs ↗pdf ↗

Quantum dilogarithm function proven from a linear difference equation.

problem Proving Faddeev's quantum dilogarithm from a linear difference equation.
method Proved Faddeev's quantum dilogarithm using Borel summation of a formal power series solution of a linear difference equation.
result Borel summation of a formal power series solution produces Faddeev's quantum dilogarithm.

For an eigenfunction of the Laplacian on a hyperbolic Riemann surface, the coefficients of the Fourier expansion are described as intertwining functionals. All intertwiners are classified. A refined growth estimate for the coefficients is given and a summation formula is proved.

2006-07-11abs ↗pdf ↗

We prove an explicit formula of the Berezin star product on Kaehler manifolds. The formula is expressed as a summation over certain strongly connected digraphs. The proof relies on a combinatorial interpretation of Englis' work on the asymptotic expansion of the Laplace integral.

2011-03-21abs ↗pdf ↗

Finet uses FBN for efficient, lightweight neural networks.

problem Building efficient neural networks with limited computational resources.
method Introduces Fine-grained Batch Normalization (FBN) and a novel light-weight network (Finet) that combines FBN with standard convolution.
result Finet achieves state-of-the-art performance on ImageNet classification with reduced computational complexity.

New techniques prove quantum modularity for various functions.

problem Proving quantum modularity of false theta functions and related series.
method Developed techniques including Poisson summation formula and modular series framework.
result Unified approach to proving quantum modularity for various functions.

In this paper, we study some vanishing identities for Gromov-Witten invariants conjectured by K. Liu and H. Xu. We will prove these conjectures in the case that the summation range is large compare to genus. In fact, in such cases, we can obtain a vanishing identity which is stronger than their conjectures. Moreover we…

2008-05-06abs ↗pdf ↗

This paper is an introduction to Khovanov homology, starting with the Kauffman bracket state summation, emphasizing the Bar-Natan Canopoloy and tangle cobordism approach. The paper discusses a simplicial approach to Khovanov homology and a quantum model for it so that the graded Euler characteristic that produces the J…

2011-07-07abs ↗pdf ↗

This paper solves the open problem of computing Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.

problem Computing the Bayes optimal prediction for decision trees is infeasible due to an infeasible summation over all division patterns of a feature space.
method Solved the open problem using a Markov chain Monte Carlo method with adaptively tuned step size.
result Computed the Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.

The paper tackles non-cumulative objectives in reinforcement learning and proposes modifications to existing algorithms.

problem Optimizing objectives that are not naturally expressed as summations of rewards in various fields.
method The paper modifies the Bellman optimality equation to handle non-cumulative objectives by replacing summation with a generalized operation.
result The modified Bellman updates can converge to the globally optimal solution under certain conditions.

Improves accuracy of SMCI estimators without expanding sum regions.

problem Intractable multiple summations in evaluating expectations on the Ising model.
method Combining multiple SMCI estimators using generalized least squares (GLS).
result The proposed method can improve accuracy without combinatorial explosion.

F. Jaeger presented the two-variable Kauffman polynomial of an unoriented link L as a weighted sum of HOMFLY-PT polynomials of oriented links associated with L. Murakami, Ohtsuki and Yamada (MOY) used planar graphs and a recursive evaluation of these graphs to construct a state model for the sl(n)-link invariant (a one…

2013-04-17abs ↗pdf ↗

The paper develops a method for forecasting power consumption at various levels of aggregation.

problem Forecasting power consumption at different levels of household aggregation.
method Three-step process: feature generation, aggregation, and projection.
result The method provides theoretical guarantees on prediction error and performs well on real data.

New framework finds more efficient linear layers over structured matrices.

problem Efficient alternatives for dense linear layers in neural networks.
method Unified framework searching over all linear operators, developing a taxonomy based on computational and algebraic properties.
result BTT-MoE provides substantial compute-efficiency gains over dense layers and standard MoE.

A thesis submitted for the degree of Doctor of Philosophy of The Australian National University. In this work we introduce several new optimisation methods for problems in machine learning. Our algorithms broadly fall into two categories: optimisation of finite sums and of graph structured objectives. The finite sum pr…

2015-10-09abs ↗pdf ↗

In the present paper the electrostatic of charges in non rotating BTZ black hole and wormhole space times is studied. In particular, the self force of a point charge in the geometry is characterized analitically. The differences between the self force in both cases is a theoretical experiment for distinguishing both ge…

2014-08-04abs ↗pdf ↗

We wish to compute the gradient of an expectation over a finite or countably infinite sample space having KK \leq \infty categories. When KK is indeed infinite, or finite but very large, the relevant summation is intractable. Accordingly, various stochastic gradient estimators have been proposed. In this paper, we de…

2018-10-10abs ↗pdf ↗

We introduce a simple permutation equivariant layer for deep learning with set structure.This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST-digit sum…

2016-11-14abs ↗pdf ↗

An Einstein nilradical is a nilpotent Lie algebra, which can be the nilradical of a metric Einstein solvable Lie algebra. The classification of Riemannian Einstein solvmanifolds (possibly, of all noncompact homogeneous Einstein spaces) can be reduced to determining, which nilpotent Lie algebras are Einstein nilradicals…

2008-02-15abs ↗pdf ↗

Bismut Einstein metrics on complex manifolds are Kähler Einstein or Bismut Ricci flat.

problem Characterizing Bismut Einstein metrics on compact complex manifolds.
method Observing the (2,0)-part of Bismut Ricci form and using it to prove properties of the metrics.
result Bismut Einstein metrics with non-zero Einstein constant are Kähler Einstein, and those with zero are Bismut Ricci flat.

Study on Einstein deformations of negative Kähler Einstein metrics.

problem Understanding Einstein deformations of Kähler Einstein metrics.
method Relate second order Einstein deformation theory to complex geometry, gauge normalise, and use Taylor expansion.
result Taylor expansion to order two of an Einstein deformation is determined by h12h_1^2 and the divergence of the Kodaira-Spencer bracket.

A lightweight model predicts IT system KPIs from historical data.

problem Predicting future KPIs of interconnected IT systems is hard due to diverse and changing components.
method A weighted heterogeneous ensemble method combining neural network and mean predictor.
result Achieved R2R^2 scores of 0.10 and 0.15 on test data.

The paper studies extended quasi-Einstein manifolds with special geometric properties and solitons.

problem Exploring new types of manifolds in general relativity.
method Generalization of existing manifolds and construction of specific examples.
result Existence and properties of extended quasi-Einstein manifolds with solitons.