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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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10202939 · Jun 202019922001200920172026
48 results for under-reaching phenomenon

The paper sets limits for GNNs solving PDEs to avoid under-reaching phenomenon.

problem Under-reaching phenomenon in GNNs solving PDEs.
method Sharp lower bounds for message-passing iterations based on PDE characteristics.
result Proposed lower bounds ensure efficient information propagation in GNNs.

EEGNN improves graph neural networks by enhancing graph structure.

problem Mis-simplification of graphs by removing self-loops and unweighted edges reduces GNN performance.
method Proposes EEGNN framework using DMPGM for better graph structural information.
result EEGNN achieves significant performance improvement over baselines.

This paper examines properties of feedforward graphs to improve neural network performance.

problem The choice of computational graph can significantly impact neural network performance.
method The paper introduces two measures: fidelity and mixing time, and evaluates popular graphs using these measures.
result Popular graphs are evaluated based on fidelity and mixing time, revealing their performance implications.

TGR rewires temporal graphs to improve TGNN performance.

problem Temporal graphs in evolving networks can suffer from under-reaching and over-squashing issues.
method TGR uses expander graph propagation to create message-passing highways between temporally distant nodes.
result TGR achieves state-of-the-art results on temporal graph benchmarks.

The two phase behavior in financial markets actually means the bifurcation phenomenon, which represents the change of the conditional probability from an unimodal to a bimodal distribution. In this paper, the bifurcation phenomenon in Hang-Seng index is carefully investigated. It is observed that the bifurcation phenom…

2007-12-30abs ↗pdf ↗

Paper derives explicit expression of Alekseev-Meinrenken diffeomorphism.

problem Understanding the Alekseev-Meinrenken diffeomorphism.
method Via the Stokes phenomenon of meromorphic linear systems of ODEs with Poncaré rank 1.
result Explicit expression of the Alekseev-Meinrenken diffeomorphism.

Study reveals GAGA phenomenon in Poisson cohomology for plane structures with isolated singularities.

problem Understanding Poisson cohomology for plane structures with isolated singularities.
method Determined Gerstenhaber algebra structure over Poisson cohomology groups.
result GAGA type phenomenon observed in Poisson cohomology.

Suppose that two large, multi-dimensional data sets are each noisy measurements of the same underlying random process, and principle components analysis is performed separately on the data sets to reduce their dimensionality. In some circumstances it may happen that the two lower-dimensional data sets have an inordinat…

2013-01-09abs ↗pdf ↗

Representation stability is a phenomenon whereby the structure of certain sequences XnX_n of spaces can be seen to stabilize when viewed through the lens of representation theory. In this paper I describe this phenomenon and sketch a framework, the theory of FI-modules, that explains the mechanism behind it.

2014-04-15abs ↗pdf ↗

New cutoff phenomenon found for geodesic paths on hyperbolic manifolds.

problem Understanding the cutoff phenomenon for geodesic paths on hyperbolic manifolds.
method Spectral strategy and detailed spectral analysis of the spherical mean operator.
result Geodesic paths on compact hyperbolic manifolds exhibit cutoff for spatially localized initial conditions.

Cohen et al. (2021) show GD trajectories align on a bifurcation diagram.

problem Understanding the Edge of Stability (EoS) phenomenon in gradient descent.
method Empirical studies and rigorous mathematical proofs for two-layer networks and single-neuron networks.
result GD trajectories align on a specific bifurcation diagram independent of initialization.

It was shown by Fomin, Shapiro and Thurston that some cluster algebras arise from orientable surfaces. Subsequently, Dupont and Palesi extended this construction to non-orientable surfaces. We link this framework to Lam and Pylyavskyy's Laurent phenomenon algebras, showing that both orientable and non-orientable unpunc…

2016-08-16abs ↗pdf ↗

For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it. This, possibly counterintuitive, phenomenon is known as peaking. In this work, we observe that a s…

2016-10-17abs ↗pdf ↗

Estimates scalar curvature without nonnegativity, showing gap phenomenon on manifolds.

problem Estimating scalar curvature without curvature nonnegativity assumption.
method Derive estimates for scalar curvature and mean curvature on manifolds and domains.
result Show that metrics on even dimensional manifolds with nonzero Euler characteristic are ε-gap distance extremal.

Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for determining the contribution of individual parameters in an ANN to catastrophic…

2019-06-06abs ↗pdf ↗

Let XX be a compact complex manifold, consider a small deformation φ:XBφ: \mathcal{X} \to B of XX, the dimension of the Dolbeault cohomology groups Hq(Xt,ΩXtp)H^q(X_t,Ω_{X_t}^p) may vary under this defromation. This paper will study such phenomenons by studying the obstructions to deform a class in Hq(X,ΩXp)H^q(X,Ω_X^p) with the param…

2007-04-16abs ↗pdf ↗

This paper identifies and analyzes the Epochal Sawtooth Phenomenon in training loss curves.

problem Training loss oscillations in adaptive gradient-based optimizers.
method Empirical analysis of Adam and other optimizers, focusing on ββ parameters, batch size, data shuffling, and sample replacement.
result The Epochal Sawtooth Phenomenon (ESP) is a recurring pattern in training loss curves, arising from adaptive learning rate adjustments and data shuffling.

When looking at Bott's original proof of his periodicity theorem for the stable homotopy groups of the orthogonal and unitary groups, one sees in the background a differential geometric periodicity phenomenon. We show that this geometric phenomenon extends to the standard inclusion of the orthogonal group into the unit…

2011-08-03abs ↗pdf ↗

Baker and Riley proved that a free group of rank 3 can be contained in a hyperbolic group as a subgroup for which the Cannon-Thurston map is not well-defined. By using their result, we show that the phenomenon occurs for not only a free group of rank 3 but also every non-elementary hyperbolic group. In fact it is shown…

2012-06-26abs ↗pdf ↗

Let XX be a compact complex manifold, consider a small deformation φ:XBφ: \mathcal{X} \to B of XX, the dimensions of the cohomology groups of tangent sheaf Hq(Xt,TXt)H^q(X_t,\mathcal{T}_{X_t}) may vary under this deformation. This paper will study such phenomenons by studying the obstructions to deform a class in $H^q(X,\mathc…

2007-04-17abs ↗pdf ↗

Categorifies Stokes coefficients in Chern-Simons theory models.

problem Stokes phenomenon in Chern-Simons theory around flat connections.
method Finite-dimensional model for analytically continued Chern-Simons theory, categorification of Stokes coefficients.
result Stokes coefficients can be promoted to graded vector spaces.

The level curves of an analytic function germ almost always have bumps at unexpected points near the singularity. This profound discovery of N. A'Campo is fully explored in this paper for $f(z,w)\in \C\{z,w\}$, using the Newton-Puiseux infinitesimals and the notion of gradient canyon. Equally unexpected is the Dirac ph…

2012-06-04abs ↗pdf ↗

Meta learning works well with overparameterized models, a phenomenon called 'benign overfitting'.

problem Understanding why overparameterized models perform well in few-shot learning.
method Analyzed the generalization performance of gradient-based meta learning with an overparameterized meta linear regression model.
result Demonstrated that overparameterized meta learning can still generalize well, a phenomenon called 'benign overfitting'.

Accuracy on in-distribution data correlates with out-of-distribution data when data is noisy or contains nuisance features.

problem Correlation between in-distribution and out-of-distribution accuracy in noisy or feature-rich data.
method Analyzes the impact of noise and nuisance features on model performance.
result Accuracy on in-distribution and out-of-distribution data can become negatively correlated in noisy or feature-rich data.

Let (M,g)(M,g) be a compact manifold and let Δφk=λkφk-Δφ_k = λ_k φ_k be the sequence of Laplacian eigenfunctions. We present a curious new phenomenon which, so far, we only managed to understand in a few highly specialized cases: the family of functions fN:MR0f_N:M \rightarrow \mathbb{R}_{\geq 0} $$ f_N(x) = \sum_{k \leq N}{ \frac{…

2017-06-05abs ↗pdf ↗

Hybrid regularization avoids double descent in random feature models.

problem Avoiding the double descent phenomenon in random feature models.
method Combines early stopping and weight decay, using GCV for hyperparameter selection.
result Hybrid method successfully avoids double descent and achieves comparable generalization.

This work reveals how label noise can cause a final ascent in neural network performance curves.

problem The impact of label noise on the performance of neural networks.
method Theoretical analysis and extensive experiments on various neural network architectures.
result Label noise can lead to a final ascent in the test loss curve, improving generalization at intermediate model widths.

Deep neural networks can grok better than shallow ones, showing multi-stage generalization.

problem Understanding the generalization behavior of deep neural networks.
method Empirical replication and analysis of grokking phenomenon in deep MLP models.
result Deep neural networks exhibit multi-stage generalization, with a secondary surge in test accuracy.

We present a dynamical system framework for understanding Nesterov's accelerated gradient method. In contrast to earlier work, our derivation does not rely on a vanishing step size argument. We show that Nesterov acceleration arises from discretizing an ordinary differential equation with a semi-implicit Euler integrat…

2019-05-17abs ↗pdf ↗

Optimal regularization can prevent the double descent phenomenon in learning models.

problem The double descent phenomenon in learning models, where test performance is non-monotonic in sample size and model size.
method Theoretical and empirical study of optimal 2\ell_2 regularization for linear regression models and neural networks.
result Optimally-tuned 2\ell_2 regularization achieves monotonic test performance for certain models and mitigates the double descent phenomenon for more general models.

Study on Neural Collapse limits in deep learning.

problem Understanding the limits of Neural Collapse in deep learning.
method Investigated Neural Collapse in the context of generalization and feature learning, refining conjectures and conducting experiments.
result Neural Collapse primarily occurs on the train set and not on the test set, suggesting it is an optimization phenomenon with unclear connections to generalization.

Let XX be a compact complex manifold and EE be a holomorphic vector bundle on XX. Given a deformation (X,E)(\mathcal{X},\mathcal{E}) of the pair (X,E)(X,E) over a small polydisk BB centered at the origin, we study the jumping phenomenon of the cohomology groups dimCHq(Xt,Et)\dim_{\mathbb{C}}H^q(\mathcal{X}_t,\mathcal{E}_t) near $t …

2016-01-25abs ↗pdf ↗

We show that there are homotopy equivalences h:NMh:N\to M between closed manifolds which are induced by cell-like maps p:NXp:N\to X and q:MXq:M\to X but which are not homotopic to homeomorphisms. The phenomenon is based on construction of cell-like maps that kill certain L\mathbb L-classes. The image space in these construc…

2006-10-31abs ↗pdf ↗