Research
On-device research index

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

Trend · papers per month

24477194 · Jun 202019922001200920172026
48 results for diagonal energies

This work examines the sensitivity of energy distance to mean differences compared to covariance differences.

problem The sensitivity of energy distance to mean differences compared to covariance differences when distributions are close.
method Analyzes the energy distance in the case where distributions are close, focusing on sensitivity to mean and covariance differences.
result Energy distance is more sensitive to mean differences than covariance differences when distributions are close.

The Bethe free energy approximation is reliable when convex on a submanifold, the 'Bethe box'.

problem Accuracy of the Bethe free energy approximation in probabilistic inference.
method Analysis of convexity and verification conditions based on the Bethe Hessian matrix.
result The Bethe approximation is mostly accurate if it is convex on a submanifold, the 'Bethe box'.

Meta learns low-rank covariance factors for better uncertainty estimation.

problem Sub-optimal covariance matrices in multi-task settings.
method Meta learns diagonal or diagonal plus low-rank factors using an attentive set encoder.
result Efficiently constructed task-specific covariance matrices improve uncertainty estimation.

Energy functional for Legendrian knots in Heisenberg group, invariant under PU(2,1).

problem Energy functional for Legendrian knots in Heisenberg group.
method Regularization of divergent integral with Korányi distance, invariant under PU(2,1).
result Characterization of minimizers and Heisenberg analog of Doyle-Schramm cosine formula.

Introduces Causal Energy Minimization to understand Transformer layers.

problem Empirical parameterization of Transformer blocks remains largely unexplored.
method Causal Energy Minimization framework that recasts Transformer layers as optimization steps on conditional energy functions.
result Identifies design space for Transformer layers including weight sharing and energy-based interpretations.

The Einstein equations in wave map gauge are a geometric second order system for a Lorentzian metric. To study existence of solutions of this hyperbolic quasi diagonal system with initial data on a characteristic cone which are not zero in a neighbourhood of the vertex one can appeal to theorems due to Cagnac and Dossa…

2010-12-02abs ↗pdf ↗

The paper studies entropy and free energy for harmonic metrics on cyclic Higgs bundles.

problem Quantifying the degree of mutual misalignment of metrics on Higgs bundles.
method Introduced entropy and free energy to quantify mutual misalignment; provided conditions for entropy and free energy to change.
result Extended work on boundedness of functions related to entropy and free energy on the unit disc.

Study index bounds for harmonic maps sequences with bubbles.

problem Upper and lower bounds of index and nullity for harmonic maps.
method Study limiting behavior of eigenfunctions of linearized operator; diagonalize index form with bilinear form varying with sequence.
result Obtain index bounds and show convergence of eigenfunctions on weak limit, bubbles, and neck regions.

D-LinOSS models learn to dissipate energy, improving performance on long-range tasks.

problem Representational limitations of LinOSS models in long-range reasoning.
method Introducing Damped Linear Oscillatory State-Space models (D-LinOSS) that learn to dissipate latent state energy on arbitrary time scales.
result D-LinOSS consistently outperforms previous LinOSS methods on long-range learning tasks, achieving faster convergence and reducing hyperparameter search space.

Diagonal linear networks converge to lasso regularization path during training.

problem Understanding the regularization behavior of diagonal linear networks.
method Analyzing the training trajectory of diagonal linear networks and comparing it to the lasso regularization path.
result The training trajectory of diagonal linear networks is closely related to the lasso regularization path.

We show that a basis of a semisimple Lie algebra of compact type, for which any diagonal left-invariant metric has a diagonal Ricci tensor, is characterized by the Lie algebraic condition of being "nice". Namely, the bracket of any two basis elements is a multiple of another basis element. This extends the work of Laur…

2019-12-29abs ↗pdf ↗

Study grid homology of diagonal knots, finding key terms related to prime factors and decompositions.

problem Determine grid homology of diagonal knots and compare them to other knot types.
method Use grid diagrams and combinatorial knot Floer homology to analyze diagonal knots.
result Grid homology detects the number of prime factors and decompositions of the knot into non-integer tangles.

Study on stability of non-diagonal Einstein metrics on specific homogeneous spaces.

problem Stability analysis of non-diagonal Einstein metrics on HimesH/ΔKH imes H/ΔK.
method Formula for scalar curvature, study of stability with Hilbert action.
result Non-diagonal Einstein metrics on MM are unstable with different coindexes.

Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms includes Adagrad, RMSprop, Adam, and recent extensions. All these algorithms have adopted diagonal matrix adaptation, due to the prohibitive co…

2019-05-26abs ↗pdf ↗

Paper proposes ABDR for convex subspace clustering with adaptive block diagonal representation.

problem Subspace clustering with block diagonal structure for noisy data.
method ABDR explicitly pursues block diagonality without sacrificing convexity, using a specially designed convex regularizer.
result Experimental results show ABDR outperforms state-of-the-arts.

The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible. This problem naturally appears in several statistical learning tasks such as blind signal separation. We consider the diagonalization criterion studied in a seminal paper by Pham (…

2018-11-28abs ↗pdf ↗

We prove a number of convexity results for strata of the diagonal pants graph of a surface, in analogy with the extrinsic geometric properties of strata in the Weil-Petersson completion. As a consequence, we exhibit convex flat subgraphs of every possible rank inside the diagonal pants graph.

2011-11-04abs ↗pdf ↗

Classify projective subvarieties in Bogomolov-Guan manifolds using quasi-diagonals.

problem Classify projective subvarieties in non-Kahler holomorphically symplectic manifolds.
method Use quasi-diagonals to classify projective subvarieties.
result Prove that any projective subvariety belongs to a fiber of the Lagrangian fibration.

In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones. Besides making a theoretical analysis of their expressivity, we introduced principled techniques for training these models: we devise an initialization s…

2019-01-29abs ↗pdf ↗

The main purpose of this note is to prove that any basis of a nilpotent Lie algebra for which all diagonal left-invariant metrics have diagonal Ricci tensor necessarily produce quite a simple set of structural constants; namely, the bracket of any pair of elements of the basis must be a multiple of some of them and onl…

2011-10-18abs ↗pdf ↗

New method improves deep learning model robustness and accuracy for long sequences.

problem Challenges in learning long-range sequence tasks using state-space models.
method Proposes a perturb-then-diagonalize (PTD) methodology to address ill-posed diagonalization problems in SSMs.
result Demonstrates improved robustness and accuracy of S5-PTD model on Long-Range Arena benchmark.

We give a quadratic lower bound on the dimension of the space of conjugacy classes of subgroups of SL(n,R) that are limits under conjugacy of the diagonal subgroup. We give the first explicit examples of abelian n-1 dimensional subgroups of SL(n,R) which are not such a limit, however all such abelian groups are limits …

2014-12-17abs ↗pdf ↗

Diagonal metrics solve Hermitian-Einstein equations for decomposed Higgs bundles.

problem Existence of diagonal pluriharmonic metrics in GG-Higgs bundles.
method Analyzes Higgs bundles over compact Kähler manifolds, decomposes vector bundles, and uses torus action to relate stability and conditions.
result Necessary and sufficient conditions for the existence of diagonal metrics solving Hermitian-Einstein equations.