arXiv research

This paper surveys various methods for dimensionality reduction and nearest neighbor search.

McKernel speeds up kernel approximations for large-scale learning.

Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such co…

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure. We use Random Kitchen Sink features to flexibly define a change surface in combination with expressive spectral mixture kernels to …

Paper proposes using DMD for feature extraction in image classification.

A new method for learning kernel spectral distributions using deep neural networks.

Bayesian deep learning with flexible nonlinearities and t-exponential family.

The paper explores how sinks and diagonal patterns prevent attention oversmoothing.

Randomly initialized transformers show extreme token preferences.

An efficient method to price bonds with optional sinking feature is presented. Such instruments equip their issuer with the option (but not the obligation) to redeem parts of the notional prior to maturity, therefore the future cash flows are random. In a one-factor model for the issuer's default intensity we show that…

This work explains the structural origins of attention sinks in LLMs.

New approach identifies offshore financial centers in global corporate network.

New proof of injectivity for broken non-abelian X-ray transform in Minkowski space.

FlowGN tackles graph representation learning by tracing information flow paths.

A new method reduces the computational cost of Sinkhorn algorithm for OT and UOT problems.

New algorithm learns sparse linear MDPs with polynomial interactions, improving sample complexity.

We define a laminar branched surface to be a branched surface satisfying the following conditions: (1) Its horizontal boundary is incompressible; (2) there is no monogon; (3) there is no Reeb component; (4) there is no sink disk (after eliminating trivial bubbles in the branched surface). The first three conditions are…

Proves analyticity of quasinormal modes in Kerr and Kerr-de Sitter spacetimes.

Gradient flow in softmax models tends to produce low-entropy outputs.

Automated design using neural networks and gradient descent.

This study analyzes information flow networks in Chinese stock sectors using transfer entropy.

A graph (digraph) $G=(V,E)$ with a set $T\subseteq V$ of terminals is called inner Eulerian if each nonterminal node $v$ has even degree (resp. the numbers of edges entering and leaving $v$ are equal). Cherkassky and Lovász showed that the maximum number of pairwise edge-disjoint $T$-paths in an inner Eulerian graph $G…

The Poincare-Hopf theorem tells us that given a smooth, structurally stable vector field on a surface of genus g, the number of saddles is 2-2g less than the number of sinks and sources. We generalize this result by introducing a more complex combinatorial invariant. Using this tool, we demonstrate that many such struc…

New Alexander polynomial defined for transverse graphs.

The paper studies grid homology for spatial graphs and proves a Künneth formula for connected sums.

Paper introduces metrics for evaluating multi-agent policies using best response dynamics.

We define sink marks for branched complexes and find conditions for them to determine a branched surface structure. These will be used to construct branched surfaces in knot and tangle complements. We will extend Delman's theorem and prove that a Montesinos knot $K$ of length at least 3 has a persistently laminar branc…

The paper uses topological concepts to analyze neural networks, revealing complex structure and dynamics.

Study compares information flow between Chinese and US stock sectors.

Algorithm reconstructs conserved networks from flow data.

Method learns multi-stage tasks from single video, overcoming challenges of raw pixel learning and insufficient demonstrations.

Classifies Morse flows on 3-sphere with specific saddle connections.

GOAT improves attention mechanisms by learning better priors.

BSG learns dynamic network spillovers and uncertainty quantification.

Economy is demanding new models, able to understand and predict the evolution of markets. To this respect, Econophysics is offering models of markets as complex systems, such as the gas-like model, able to predict money distributions observed in real economies. However, this model reveals some technical hitches to expl…

In this article we will show that the Macro-Economy and its growth can be modelled and explained exactly in principle by commonly known Field Theory from theoretical physics. We will show the main concepts and calculations needed and show that calculation and prediction of economic growth then gets indeed possible in D…

Method solves long-horizon robotic tasks via imitation and reinforcement learning.

In the recent years, the desire and need to understand sequential data has been increasing, with particular interest in sequential contexts such as patient monitoring, understanding daily activities, video surveillance, stock market and the like. Along with the constant flow of data, it is critical to classify and segm…

The paper proves the existence of a horizon for certain spacetimes.

Model infers utility from lion GPS data using Gaussian processes.

Study on uniquely determining thermal properties from boundary temperature and heat flux measurements.

Crypto markets show negative spillovers between chains, not positive co-movements.

It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…

Poisson learning improves graph-based semi-supervised learning at very low label rates.

The paper interprets a graph's Alexander polynomial topologically.

New metrics improve scRNA-seq perturbation modeling by reducing mode collapse.

This dissertation uses ILP to learn Bayesian network structures efficiently.

We consider the class non-surjective irreducible endomorphisms of the free group $F_n$. We show that such an endomorphism $φ$ is topologically represented by a simplicial immersion $f:G \rightarrow G$ of a marked graph $G$; along the way we classify the dynamics of $\partial φ$ acting on $\partial F_n$: there are at mo…