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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.

169,051 papers · 148 categories

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48 results for cartoon drawing

Paper tackles selfie cartoonization with a new GAN.

problem Cartoonization of selfies with artistic abstraction and exaggeration.
method Proposes scGAN with attentive adversarial network (AAN) for specific facial region emphasis.
result Method generates cartoon portraits with improved robustness and different styles.

Wiatowski and Bölcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities. While the translation invariance result appli…

2016-04-29abs ↗pdf ↗

A new method for computing image curvature efficiently and accurately.

problem Low performance, low accuracy, and requirement of second order differentiability in conventional computation schemes.
method Proposes a novel discrete computation scheme for weighted Gaussian curvature.
result More accurate, computationally more efficient, and does not require second order differentiability.

Box Thirding identifies the best arm efficiently under limited samples.

problem Efficiently identifying the best arm with limited sampling.
method Iterative ternary comparison of arms, discarding the weakest and exploring the best.
result Achieves comparable performance to Successive Halving with less predefined parameters.

We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on thousands of crude human-drawn images representing hundreds of classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training …

2017-04-11abs ↗pdf ↗

SPX optimizes multiple graph drawing metrics for better readability.

problem Graph drawing algorithms often optimize one metric at a time, leading to suboptimal layouts.
method Introduces Stress-Plus-X (SPX) framework that optimizes stress, crossings, angles, and upwardness simultaneously.
result SPX achieves results close to state-of-the-art algorithms that optimize metrics individually.

There are three main thrusts to this article: a new proof of Levi's Enlargement Lemma for pseudoline arrangements in the real projective plane; a new characterization of pseudolinear drawings of the complete graph; and proofs that pseudolinear and convex drawings of KnK_n have n2+n^2+{}O(nlogn)(n\log n) and O(n2)(n^2), respect…

2015-11-21abs ↗pdf ↗

We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test the…

2017-06-16abs ↗pdf ↗

New method learns high-quality Laplacian representations for reinforcement learning.

problem Lack of accurate Laplacian representations in large or continuous state spaces.
method Reformulated spectral graph drawing objective to have eigenvectors as unique global minimizer.
result Learned Laplacian representations more faithfully approximate the ground truth.

A new method for spotting symbols in CAD images reduces annotation costs and improves accuracy.

problem Challenging task of labeling symbols from CAD drawings.
method Pixel-wise point location via Progressive Gaussian Kernels (PGK) and local offset.
result The proposed method achieves good generalization on real-world CAD images.

In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attrac…

2018-07-11abs ↗pdf ↗

Examines parallels between human subjects and texts for causal inference.

problem Ambiguity and fallacies in causal inference using textual data.
method Two strategies: shifting from traits to perceptions and from concepts to parts.
result Highlights the importance of clarifying fundamental concepts.

We usually think of 2-dimensional manifolds as surfaces embedded in Euclidean 3-space. Since humans cannot visualise Euclidean spaces of higher dimensions, it appears to be impossible to give pictorial representations of higher-dimensional manifolds. However, one can in fact encode the topology of a surface in a 1-dime…

2017-04-04abs ↗pdf ↗

SketchTransfer task tests deep networks' ability to generalize missing details.

problem Deep networks struggle with generalizing missing details in images.
method Proposes SketchTransfer dataset and task to study detail-invariance.
result State-of-the-art methods perform poorly on SketchTransfer task.

A grid layout method for graph classification using CNNs.

problem How to project graphs onto grids for CNNs to work effectively.
method Proposes a novel graph-preserving grid layout (GPGL) using integer programming to minimize topological loss, and solves it approximately with a regularized Kamada-Kawai algorithm.
result Demonstrates the success of the method for graph classification using multi-scale maxout CNNs.

GraphSAC detects anomalies in large graphs by sampling and filtering node subsets.

problem Vulnerability of holistic anomaly detection methods to compromised nodal attributes and network links.
method Randomly draws subsets of nodes, filters out contaminated sets, and uses SSL to estimate nominal label distributions.
result GraphSAC provides performance guarantees and is scalable to large graphs.

New bounds on learning algorithm generalization error derived using information density.

problem Bounding the generalization error of learning algorithms.
method Exponential inequalities and information density/conditional information density.
result Novel bounds on average and tail probability of generalization error.

Sampling more can make models more confident in wrong answers, not better.

problem The modal ceiling and correlation ceiling limit the benefit of increased sampling.
method Analyzes the trade-offs between sampling more and selecting the best answer.
result Extra sampling beyond a certain point does not improve model performance and can even degrade it.

Optimizes tax payments for insurance companies using Lévy risk processes.

problem Maximizing expected accumulated discounted tax payments with a modified objective function.
method Loss-carry-forward tax system applied to spectrally negative Lévy processes until general draw-down time.
result Optimal tax return function and strategy derived.

We present a simplified exposition of some classical and modern results on graph drawings in the plane. These results are chosen so that they illustrate some spectacular recent higher-dimensional results on the border of topology and combinatorics. We define a mod2-valued self-intersection invariant (i.e. the van Kampe…

2018-05-25abs ↗pdf ↗