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 …
On-device research index
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.
168,982 papers · 148 categories
Trend · papers per month
48 results for “artist efficiency”
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…
Predict artist efficiency in VFX shots using matrix completion.
problem Predicting artist efficiency in rendering VFX shots.
method Structured matrix factorization models for bounded entries.
result Effective models for predicting artist efficiency.
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…
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…
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…
A study compares local music recommendation algorithms, finding neighborhood-based methods perform best.
problem Cold-start problem for local artists with little user preference data.
method Comparison of three recommender system algorithms (IIN, ALS, BPR) on local music recommendation task.
result Neighborhood-based approach (IIN) performs best for local music recommendation.
Computer graphics techniques improve art pricing by measuring painting effort.
problem Traditional art pricing models lack measures for conceptual and painting efforts.
method Applied image recognition to measure line and color variances as proxies for effort.
result Painting effort (line and color variances) significantly positively correlates with sales price.
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.
problem Evaluating blue-chip art as a viable asset class for diversification.
method Developed Arte-Blue Chip Index tracking top-performing artists over 24 years.
result 20% allocation of blue-chip art in a diversified portfolio increases risk-adjusted returns by 20%.
Improved cover song detection with neural networks.
problem Identifying cover songs from original recordings.
method Siamese Convolutional Neural Networks trained on cover song audio clips.
result Mean precision@1 of 65% over mini-batches, significantly outperforming random guessing.
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…
NFT art market shows strong preferential ties among sellers and buyers.
problem Reducing preferential ties in NFT art market.
method Analyzing NFT art sales data from multiple galleries.
result NFT art market is highly concentrated with preferential ties.
LION generates high-quality 3D shapes using hierarchical latent diffusion models.
problem Creating high-quality 3D shapes for digital artists.
method Hierarchical Latent Point Diffusion Model (LION) with a global shape latent and point-structured latent space.
result LION achieves state-of-the-art generation performance on ShapeNet benchmarks.
New algorithms ensure generated objects evolve and fill a distribution, unlike static neural networks.
problem Ensure generated objects evolve and fill a distribution, unlike static neural networks.
method Propose a numerical paradigm based on Radon-Sobolev statistical distances to ensure objects do not repeat and evolve.
result Objects created by VAEs evolve and fill the target probability distribution, unlike static neural networks.
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…
Paper proposes a new ML framework to enhance creativity in music generation.
problem Current generative models struggle to produce music outside their training dataset.
method Develops a new ML objective to address creativity limitations.
result Proposed framework could improve generative models' creativity.
MAGIC generates image collages from set templates using attention and set representations.
problem Generating image collages from set templates is challenging for classical models.
method Memory Attentive Generation of Image Collages (MAGIC) using Set-Transformer layers and set-pooling.
result MAGIC can generate image collages from set templates in one forward pass.
We introduce MosAIc, an interactive web app that allows users to find pairs of semantically related artworks that span different cultures, media, and millennia. To create this application, we introduce Conditional Image Retrieval (CIR) which combines visual similarity search with user supplied filters or "conditions". …
KaoKore dataset extracts faces from pre-modern Japanese art for machine learning.
problem Lack of relevant datasets for historical Japanese artworks in machine learning.
method Extracted faces from pre-modern Japanese artwork to create a new dataset.
result Demonstrated the dataset's value for image classification and creative applications.
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.
A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.
problem Myopically optimizing user utility can be unfair to item providers in two-sided markets.
method A controller that integrates unbiased estimators for fairness and utility, dynamically adapting as more data becomes available.
result Empirically, the algorithm is highly practical and robust, ensuring amortized group fairness.
New AI tools generate and vary dance choreographies.
problem Creating original and customizable dance choreographies.
method Recurrent neural networks and autoencoders trained on movement data.
result Generated and varied dance sequences using machine learning.
AI recovers lost art from x-rays.
problem Reconstructing lost artwork under layers of x-ray imaging.
method Neural style transfer applied to x-radiographs.
result Reconstructed lost artwork visible through x-rays.
Generative adversarial network synthesizes sketches into realistic images.
problem Improving the quality and realism of facial sketches.
method Hybrid GAN with quality guided encoder and identity preserving network.
result Synthesized images are more realistic and maintain identity.
Commissioned by MIT's in-house artist Jane Philbrick, we evolve an abstract 2D surface (resembling Marta Pan's 1961 "Sculpture Flottante I") under mean curvature, all the while calculating the eigenmodes and eigenvalues of the Laplace-Beltrami operator on the resulting shapes. These are then synthesized into a sound-wa…
DeepDrummer generates drum loops with human preferences via active learning.
problem Creating drum loops efficiently with user preferences.
method Active learning with a deep neural network classifier.
result System converges to user preferences after few interactions.
Research connects physics and math through ceramic art of Riemann surfaces.
problem Classifying curves on Riemann surfaces using 'pairs of pants'.
method Exploring mathematical concepts through artistic ceramics.
result Illustrates the classification of curves on Riemann surfaces.
Jukebox generates high-fidelity songs with singing in raw audio.
problem Generating music with singing in raw audio.
method Multi-scale VQ-VAE for compression, autoregressive Transformers for modeling.
result Generates high-fidelity and diverse songs with coherence up to multiple minutes.
AI generates sculptural objects through machine learning.
problem Generating creative and printable 3D sculptures.
method Developed two algorithms: Amalgamated DeepDream (ADD) and Partitioned DeepDream (PDD).
result Generated creative and printable 3D point clouds.
Deep learning-based style transfer between images has recently become a popular area of research. A common way of encoding "style" is through a feature representation based on the Gram matrix of features extracted by some pre-trained neural network or some other form of feature statistics. Such a definition is based on…
The paper tackles carousel personalization in music streaming apps using contextual bandits.
problem Selecting relevant items to display in carousels for personalized content recommendation.
method Modeling carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback.
result Empirically shows the effectiveness of the framework in capturing characteristics of real-world carousels.
Paper uses RL for high-level character control in 3D environments.
problem Creating intelligent characters with generalizable behavior.
method Combines traditional animations, heuristics, and reinforcement learning.
result Demonstrates learning of complex behaviors in a 3D environment.
NFTs revolutionize art sales by providing proof of ownership.
problem Lack of provenance and authenticity in digital art.
method Analysis of major art NFT marketplaces.
result NFTs reduce the need for intermediaries in the art trade.
Neural painters learn to generate brushstrokes from a non-deterministic painting program.
problem Training an agent to generate realistic brushstrokes from a non-differentiable painting program.
method A differentiable neural painter model trained on brushstrokes, optimizing for human-like strokes and intrinsic style transfer.
result Direct optimization of brushstrokes can visualize ImageNet categories and generate ideal paintings.
Deep learning enhances art market valuation by incorporating visual data.
problem Improving valuation accuracy in the art market, especially for first-time sales.
method Benchmarked classical and modern deep learning models using a large auction dataset.
result Visual embeddings add distinct economic value for first-time art sales.
Proposes Gaussian optimal transport for image style transfer.
problem Image style transfer and mixing different artistic styles.
method Encoder/Decoder framework with optimal transport for Gaussian measures.
result Simple methodology for generating stylized content interpolating between many styles.
The field of image classification has shown an outstanding success thanks to the development of deep learning techniques. Despite the great performance obtained, most of the work has focused on natural images ignoring other domains like artistic depictions. In this paper, we use transfer learning techniques to propose …
NFT royalties boost creator earnings by sharing risk, reducing info asymmetry, and enabling price discrimination.
problem NFTs' royalties are criticized for being neutralized by speculators.
method Analyzes NFTs' royalties in various market conditions and their effects on creators.
result Royalties enable creators to capitalize on speculators' presence through risk sharing, info reduction, and price discrimination.
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has …
Procedural content generation via machine learning (PCGML) is typically framed as the task of fitting a generative model to full-scale examples of a desired content distribution. This approach presents a fundamental tension: the more design effort expended to produce detailed training examples for shaping a generator, …
FIGARO generates symbolic music with fine-grained control.
problem Minimal control over generated music sequences.
method Description-to-sequence task, learning conditional distribution of sequences given high-level descriptions.
result State-of-the-art controllable symbolic music generation.
Paper classifies Brazilian music genres using song lyrics with BLSTM network.
problem Classifying Brazilian music genres from lyrics.
method Used BLSTM network combined with SVM, Random Forest, and word embeddings.
result BLSTM outperforms other models with an F1-score of 0.48.
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative informationcs.LG
A new method clusters qualitative data with mixed variables, improving interpretability.
problem Clustering qualitative data with context and high-dimensional mixed datasets.
method Hierarchical Qualitative Clustering (HQC) using Maximum Mean Discrepancy.
result HQC maintains interpretability of qualitative information and clusters effectively.
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural netw…
New method for one-shot timbre transfer in music.
problem One-shot timbre transfer in music.
method Self-supervised VQ-VAE extension for disentangled representations.
result Outperforms selected baselines on objective metrics.
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classificationstat.ML
This paper analyzes how training data can be leaked from gradients in neural networks and proposes a metric for measuring model security.
problem Training data leakage from gradients in neural networks for image classification.
method Formulated the problem as an optimisation problem for each layer, involving weights, gradients, and constraints from preceding layers.
result Attributed training data leakage to the architecture of the deep network and proposed a metric for measuring model security.