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pro vyhledávání: '"Williams, Matthew P"'
In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifica
Externí odkaz:
http://arxiv.org/abs/2408.00108
Autor:
Cavalieri, Renzo, Williams, Matthew M.
This paper studies the relationship between quadratic Hodge classes on moduli spaces of pseudostable and stable curves given by the contraction morphism $\mathcal{T}.$ While Mumford relations do not hold in the pseudostable case, we show that one can
Externí odkaz:
http://arxiv.org/abs/2404.13201
Autor:
Williams, Matthew, Makarenkov, Oleg
We prove that if a certain entry in the map of the Hadamard-Perron theorem is $T$-periodic in one of the variables, then the stable manifold guaranteed by the Hadamard-Perron theorem is a graph of a $T$-periodic function. As an application, we extend
Externí odkaz:
http://arxiv.org/abs/2311.03728
Feature attribution methods are widely used to explain neural models by determining the influence of individual input features on the models' outputs. We propose a novel feature attribution method, CAFE (Conflict-Aware Feature-wise Explanations), tha
Externí odkaz:
http://arxiv.org/abs/2310.20363
Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. \sw{Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection
Externí odkaz:
http://arxiv.org/abs/2310.01575
Neural networks (NNs) can learn to rely on spurious signals in the training data, leading to poor generalisation. Recent methods tackle this problem by training NNs with additional ground-truth annotations of such signals. These methods may, however,
Externí odkaz:
http://arxiv.org/abs/2311.12813
By studying laminations of the unit disk, we can gain insight into the structure of Julia sets of polynomials and their dynamics in the complex plane. The polynomials of a given degree, $d$, have a parameter space. The hyperbolic components of such p
Externí odkaz:
http://arxiv.org/abs/2309.11660
We present csSampling, an R package for estimation of Bayesian models for data collected from complex survey samples. csSampling combines functionality from the probabilistic programming language Stan (via the rstan and brms R packages) and the handl
Externí odkaz:
http://arxiv.org/abs/2308.06845
Autor:
Savitsky, Terrance D., Williams, Matthew R., Gershunskaya, Julie, Beresovsky, Vladislav, Johnson, Nels G.
Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. E
Externí odkaz:
http://arxiv.org/abs/2208.14541
Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whe
Externí odkaz:
http://arxiv.org/abs/2207.05050