Transfer learning artist group factors for more accurate music genre classification.
problem Automated music genre classification from audio is challenging due to subjective and noisy genre labels.
method Apply transfer learning to learn artist-related factors for genre classification, using different transfer methods.
result Achieved highest validation accuracy on FMA dataset through multi-task learning.
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.
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…
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.
New method clusters unknown music artists using audio metrics.
problem Disambiguating large catalogs of unknown artists.
method Metric learning from audio data with negative sampling.
result Our method outperforms a classifier-based approach when audio data is available.
Paper tackles artist style transfer using quadratic potential.
problem Applying a specific artist's style to real-world photographs.
method Adversarial training with quadratic potential divergence and integration of deep learning techniques.
result Demonstrates improved quality of artist stylized images.
CRNN improves artist classification with temporal audio features.
problem Artist classification using deep learning with temporal structure.
method Convolutional Recurrent Neural Network (CRNN) applied to music artist identification dataset.
result Best model achieves an average F1 score of 0.937.
Paper discovers and manipulates artistic styles in paintings without supervision.
problem Automatic discovery and manipulation of artistic styles in large art collections.
method Unsupervised learning using archetypal analysis on deep image representations.
result Learned dictionary of archetypal styles can be used for style interpretation and manipulation.
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 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.
FAMOS combines parametric and non-parametric methods for efficient image stylization.
problem Efficiently stylize images with limited data and compute resources.
method Fully Adversarial Mosaics (FAMOS) that integrates parametric and non-parametric approaches.
result Demonstrates the effectiveness of FAMOS in stylizing images with minimal data and compute resources.
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.
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.
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.
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.
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.
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.
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.
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…
Homotopy equivalent to spheres, free factor complex of a free group.
problem Understanding the homotopy type of free factor complexes.
method Proving homotopy equivalence and connectivity of relevant complexes.
result Homotopy equivalence of free factor complexes to spheres and other complexes.
We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternati…
Factor analysis provides linear factors that describe relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also na…
Corrects a 1998 proof about free factors of free groups.
problem Verifying the proof of free factors in free groups.
method Analyzes the geometric realization of free factors.
result Geometric realization is homotopy equivalent to a wedge of spheres.
Maps between automorphism groups are isomorphisms for free factor complexes.
problem Understanding the structure of automorphism groups of free factor complexes.
method Establishing isomorphisms between automorphism groups and automorphism groups of free factor complexes.
result Natural maps from mAut(Fn) to the automorphism group of the free-factor complex AFn are isomorphisms. Proposes Robust Matrix Factorization with Grouping Effect (GRMF) for better performance and robustness.
problem Improves matrix factorization by incorporating grouping effect for better performance and robustness.
method Integrates grouping effect into matrix factorization, using an efficient alternating minimization framework with DC programming and ADMM.
result Demonstrates improved performance and robustness compared to five benchmark algorithms on real-world data sets with outliers and noise.
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.
New framework for interpretable firm characteristics factors.
problem Creating statistically efficient and economically interpretable factors from firm characteristics.
method Grouping related characteristics and deriving one factor per group, combining economic intuition with data-driven clustering.
result Parsimonious, transparent factors outperform benchmarks in out-of-sample tests.
Factorizes discrete representations of finitely generated groups into PSL(2, R).
problem Understanding discrete representations of finitely generated groups into PSL(2, R).
method Factorization theorem for Fuchsian groups, Makanin-Razborov diagrams, and new class of groups called PSL(2, R)-discrete limit groups.
result Obtained useful information about PSL(2, R)-discrete limit groups.
Study reveals uniform difference in stretch factors between genus two handlebody group and outer automorphism group.
problem Analyzing the relationship between stretch factors in genus two handlebody group and outer automorphism group.
method Examined natural homomorphism from genus g handlebody group to outer automorphism group of free groups, focusing on pseudo-Anosov mapping classes and their stretch factors.
result Minimum stretch factor in genus two handlebody group is less than ten times the stretch factor of fully irreducible outer automorphism.
We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the …
New nonconvex regularizers improve low-rank matrix recovery efficiency and accuracy.
problem Efficiently recover low-rank matrices from incomplete data.
method Factor group-sparse regularization, related to Schatten-p norms.
result Improved generalization error bounds for Schatten-p norms as p decreases.
Study connects group invariants through outer automorphisms and polynomial relations.
problem Understanding polynomial invariants of free-by-cyclic groups.
method Introducing orientable fully irreducible outer automorphisms to relate McMullen polynomial and Alexander polynomial.
result Characterization of when homological stretch factor equals geometric stretch factor.
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.
Paper develops a new estimator for high-dimensional panel data with common shocks.
problem Cross-sectionally dependent errors driven by common shocks in high-dimensional panel data.
method Factor-augmented sparse-group LASSO estimator combining MIDAS aggregation with latent factors.
result The estimator outperforms standard LASSO for prediction and estimation in settings with cross-sectional dependence.
Study shows top homology group isn't dualizing module for automorphism group of free groups.
problem Identifying the dualizing module for automorphism group of free groups.
method Analyzing the top homology group of the free factor complex.
result The top homology group is not the dualizing module for extAut(Fn), especially for n=5. Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.
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.
Hyperbolicity proven for a specific type of group extension.
problem Proving hyperbolicity of a specific group extension.
method Analyzing ascending HNN extension of groups with a free factor system and an injective endomorphism.
result Ascending HNN extension of a group is hyperbolic relative to a collection of maximal parabolic subgroups.
Conditions for hyperbolic and relatively hyperbolic extensions of free groups using automorphisms with fixed points.
problem Conditions for hyperbolic and relatively hyperbolic extensions of free groups.
method Using dynamics of outer automorphisms on the complex of free factors and investigating the geometry of the extension group.
result Conditions for hyperbolic and relatively hyperbolic extensions of free groups using automorphisms with fixed points.