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.
NFT art market shows strong preferential ties among sellers and buyers.
This paper makes the first attempt to introduce the tools from computer graphics into the art pricing research. We argue that the creation of a painting calls for a combination of conceptual effort and painting effort from the artist. However, as the important price determinants, both efforts are long missing in the tr…
LION generates high-quality 3D shapes using hierarchical latent diffusion models.
Cutting and pasting image segments feels intuitive: the choice of source templates gives artists flexibility in recombining existing source material. Formally, this process takes an image set as input and outputs a collage of the set elements. Such selection from sets of source templates does not fit easily in classica…
NFTs revolutionize art sales by providing proof of ownership.
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn artist embeddings directly from audio, and using a dedicated homonym artists dataset, we compare our method with a recent approach that lea…
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Non-parametric exem…
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist …
In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph. We train our neural networks in adversarial setting via recently introduced quadratic potential divergence for stable learning process. To further improve the quality of genera…
The artistic style of a painting is a subtle aesthetic judgment used by art historians for grouping and classifying artwork. The recently introduced `neural-style' algorithm substantially succeeds in merging the perceived artistic style of one image or set of images with the perceived content of another. In light of th…
KaoKore dataset extracts faces from pre-modern Japanese art for machine learning.
Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This pa…
We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter leads to much faster convergence. We propose a method for encoura…
We explore the task of local music recommendation: provide listeners with personalized playlists of relevant tracks by artists who play most of their live events within a small geographic area. Most local artists tend to be obscure, long-tail artists and generally have little or no available user preference data associ…
I find a topological arrangement of assets traded in a phonographic market which has associated a meaningful economic taxonomy. I continue using the Minimal Spanning Tree and the Life-time Of Correlations between assets, but now outside the stock markets. This is the first attempt to use these methods on phonographic m…
Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to mode…
Paper introduces Arte-Blue Chip Index for diversifying portfolios with art investments.
Improved cover song detection with neural networks.
In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method is based on archetypal analysis, which is an unsupervised learning technique akin to sparse coding with a geometric interpretation. When appl…
New algorithms ensure generated objects evolve and fill a distribution, unlike static neural networks.
Paper proposes a new ML framework to enhance creativity in music generation.
Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration…
This paper measures the information quantity in paintings using entropy.
Digital money could reduce germ spread during coronavirus.
A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.
Study of digital topology concepts like hyperspaces and function graphs.
Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences of choreography as well as tunable variations on input choreographic sequences. We use recurrent neural network and autoencoder architectur…
The paper highlights issues with fixed point claims in digital images.
Find limiting sets for digital cones and suspensions.
Corrects incorrect assertions about fixed points in digital topology.
Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork …
Study AFPP of unions of convex digital disks in 2D.
The paper addresses flaws in fixed point assertions for digital images.
Study minimal freezing sets in convex digital disks.
Study on cold and freezing sets in digital images.
Incorrect fixed point assertions in digital topology are discussed.
In this paper, we show how to construct graph theoretical models of n-dimensional continuous objects and manifolds. These models retain topological properties of their continuous counterparts. An LCL collection of n-cells in Euclidean space is introduced and investigated. If an LCL collection of n-cells is a cover of a…
Incorrect fixed point assertions in digital topology are discussed.
Digital trees have approximate fixed point property, and conditions for products are explored.
Study convexity and AFPP in digital images.
The paper highlights issues in fixed point claims in digital topology.
Han discusses variants of digital covering maps and their equivalences.
Critiques incorrect fixed point assertions in digital topology.
Examines how irreducibility and rigidity affect digital images.
Study restrictions on digitally continuous functions and their effects.
Fixed point assertions in digital topology are often incorrect or poorly stated.
A new method streamlines digital payment programming using smart contracts.
New framework explains leading digit patterns without probabilistic assumptions.