Improved TTS style transfer across disjoint datasets with adversarial cycle consistency.
problem Suboptimal TTS style transfer on disjoint datasets with underrepresented styles.
method Adversarial cycle consistency training with paired and unpaired triplets.
result 78% improvement in style transfer with minimal reduction in fidelity and naturalness.
The paper tackles calibrating long-term behaviors with multiple styles using programmatic style-consistency.
problem Generating long-term sequential behaviors with multiple styles simultaneously.
method Leverage programmatic labeling functions to specify controllable styles and derive style-consistency as a learning objective.
result Learned policies can be calibrated for up to 1024 distinct style combinations.
Novel approach checks mutual fund style consistency with actual composition.
problem Ensuring mutual fund style consistency with actual fund composition.
method Time series decomposition of stock prices to analyze mutual fund composition.
result Notable deviations found between proclaimed fund style and actual composition.
StyleNeRF generates high-resolution images with 3D consistency and style control.
problem Generating high-resolution images with fine details and 3D consistency.
method Integrates NeRF into a style-based generator for efficient high-resolution image synthesis.
result Synthesizes high-resolution images at interactive rates with high 3D consistency and style control.
Deep learning improves handwriting style transfer and extraction.
problem Improving handwriting style transfer and extraction using deep neural networks.
method Used a deep conditioned autoencoder on IRON-OFF handwriting data-set to explore style transfer and extraction.
result Improved metrics of state-of-the-art methods by a large margin in style transfer and extraction experiments.
Study examines Indian equity mutual funds' investment style and risk-shifting.
problem Understanding how Indian equity mutual funds' investment styles affect their returns.
method Estimating size and style beta coefficients, identifying breakpoints, analyzing investment styles, and assessing risk-shifting intensity.
result Funds can enhance returns by shifting to high-return styles like Small Value and Small Blend.
Paper proposes a method to extract style features from unlabeled data.
problem Extracting fine-grained features like styles from unlabeled data.
method Contrastive conditioned variational autoencoders with mutual information constraints.
result The method efficiently extracts style features from real-world natural image datasets.
Style Miner generates stable and significant style factors for time series analysis.
problem Finding significant and stable explanatory factors in high-dimensional time series data.
method Proposes a reinforcement learning method to balance explanatory power and stability constraints.
result Outperforms existing methods by a large margin and achieves a 10% gain in R-squared explanatory power.
The paper defines and implements risk-indifference pricing for American-style contingent claims.
problem Pricing American-style contingent claims under uncertainty.
method Indifference pricing using convex risk measures and stochastic volatility models, with numerical solutions via deep learning.
result Characterization of indifference prices via Backward Stochastic Differential Equations (BSDEs).
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
LostGANs generate realistic images from reconfigurable layouts and styles.
problem Learning generative models for realistic images from reconfigurable layouts and styles.
method End-to-end training of GANs with two new components: mask maps and ISLA-Norm.
result State-of-the-art performance on COCO-Stuff and Visual Genome datasets.
Method converts emotions in nonparallel speech data.
problem Lack of parallel data for speech emotion conversion.
method Unsupervised style transfer technique for nonparallel training.
result Effectiveness demonstrated on nonparallel corpora with four emotions.
Improves PSR learning by refining spectral initialization with PSIM-style updates.
problem Inference performance of PSRs is poor despite good theoretical guarantees.
method Combines spectral algorithms for PSRs with PSIM-style updates for inference-based loss optimization.
result Inference Gradients outperforms PSRs and PSIMs on real and synthetic data.
We consider the pricing of European-style structured credit payoff in a static framework, where the underlying default times are independent given a common factor. A practical application would consist of the pricing of nth-to-default baskets under the Gaussian copula model (GCM). We provide necessary and sufficient co…
Neural-style algorithm classifies artistic styles of paintings.
problem Classifying artistic styles of paintings using neural networks.
method Uses neural-style algorithm to merge artistic style with content of images.
result Neural-style representation effectively classifies artistic styles of paintings.
Efficiently transfers style to content without distorting the content structure.
problem Arbitrary style transfer in computer vision.
method Rigid alignment of style features to content features.
result High-quality stylized images with intact content structure.
SMAPGAN generates styled map tiles from remote sensing images.
problem Generating timely updated map tiles from remote sensing images is challenging.
method Semi-supervised GAN model with gradient loss and ESSI metric.
result SMAPGAN outperforms state-of-the-art methods in quality metrics and human perception.
Paper applies GANs to symbolic music genre transfer.
problem Symbolic music genre transfer using GANs.
method CycleGAN architecture with additional discriminators to preserve structure.
result Fidelity of transformed music improved with additional discriminators.
The study analyzes how data augmentation helps isolate content from style in self-supervised learning.
problem Understanding how data augmentation affects the separation of content and style in self-supervised learning.
method Formulated a latent variable model with content and style components, studied identifiability of latent representation, and introduced a dataset to test the theory.
result Sufficient conditions for identifying the invariant content partition in self-supervised learning.
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.
Method learns anime style from portraits using GANs.
problem Capturing style differences between artists.
method Metric learning for style encoding, GANs for generation.
result Style representation captures artist differences.
Study evaluates methods for improving model robustness to various real-world distribution shifts.
problem Improving model robustness to real-world distribution shifts like geographic changes.
method Introduced new datasets and evaluated existing methods on four types of shifts (style, blurriness, location, camera operation).
result Data augmentations and larger models can improve robustness on real-world distribution shifts, contrary to prior claims.
Model generates outfits and extracts style information from them.
problem Lack of methods focusing on global outfit style information.
method Incorporated unsupervised style extraction module into a bidirectional LSTM and autoencoder model.
result Generated outfits more flexibly and extracted easily interpretable styles.
STYLE-ANALYZER fixes code style inconsistencies without manual intervention.
problem Manual code reviews are time-consuming and error-prone.
method Unsupervised machine learning with decision tree forest model.
result STYLE-ANALYZER accurately fixes code formatting violations with interpretable rules.
We introduce an innovative theoretical framework to model derivative transactions between defaultable entities based on the principle of arbitrage freedom. Our framework extends the traditional formulations based on Credit and Debit Valuation Adjustments (CVA and DVA). Depending on how the default contingency is accoun…
SCR diversifies recommendations by applying style transfer to user profiles.
problem Diversify personalized recommendations without losing relevance.
method Style injection using Conditional Variational Autoencoder (CVAE) architecture.
result 12% improvement in NDCG@20 and 22% improvement in AUC across all classes.
Enhances out-of-domain calibration of neural networks.
problem Improving calibration performance of deep neural networks in out-of-domain settings.
method Consistency-guided temperature scaling (CTS) that considers style and content consistency.
result Significantly enhances out-of-domain calibration performance.
Study uses LLMs to improve Black-Litterman portfolio optimization.
problem Systematically generating investor views for Black-Litterman model.
method Translates LLM return forecasts and uncertainty into Black-Litterman inputs.
result LLM-driven portfolios outperform traditional baselines.
Preserves content while changing style in text.
problem Text style transfer often fails to preserve content.
method Leverages linguistic information in structured supervisions.
result Significant improvement in content preservation and style transfer.
Analyzes fashion demand to optimize style mix in retail.
problem Optimizing fashion assortment planning in retail.
method Derives a style quotient from customer demand data, decoupling style from price.
result Shows the relationship between customer perception and retailer's style mix.
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.
Deep learning system generates new Chinese fonts via style variables.
problem Efficiently design new Chinese fonts.
method End-to-end deep learning system generating new style fonts via interpolation of latent style-related embedding variables.
result Smooth transition between different font styles achieved.
Bayesian CycleGAN improves cycle-consistent GANs by stabilizing training and diversifying generated images.
problem Challenges in stabilizing training of cycle-consistent GANs leading to mode collapse.
method Proposes a Bayesian approach to stabilize training and diversify generated images.
result Improves per-pixel accuracy by 15% on Cityscapes semantic segmentation task and 20% on Monet2Photo style transfer.
Method recombines image content and style from different images.
problem Recombining image content and style from different images.
method Constructs content embedding, uses VAE with leakage filtering to ensure separation of style and content.
result Synthesizes novel images with state-of-the-art performance on few-shot learning tasks.
A new transformer model accelerates training with optimization techniques.
problem Training deep neural networks efficiently and effectively.
method Interprets transformer layers as optimization steps, applying Nesterov acceleration.
result The new model outperforms existing models on benchmark datasets.
Simple method disentangles content and style from pre-trained vision models.
problem Learning interpretable features in visual representations.
method Probabilistic linear entanglement model and simple disentanglement algorithm.
result Method provably disentangles content and style features.
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
In this paper, we price American-style Parisian down-and-in call options under the Black-Scholes framework. Usually, pricing an American-style option is much more difficult than pricing its European-style counterpart because of the appearance of the optimal exercise boundary in the former. Fortunately, the optimal exer…
Laprop separates Adam's momentum and adaptivity to improve stability and speed.
problem Unnecessary coupling between Adam's momentum and adaptivity leads to instability and divergence.
method Proposes Laprop, a method that decouples momentum and adaptivity.
result Laprop consistently improves speed and stability over Adam on various tasks.
MIDI-VAE models music dynamics and instrumentation for style transfer.
problem Modeling and transferring musical style across different instruments and dynamics.
method Variational Autoencoder (VAE) for polyphonic music modeling and style transfer.
result MIDI-VAE successfully transfers musical style between different genres and instruments.
Efficient algorithm for orthogonal canonical correlation analysis (OCCA).
problem Solving the OCCA problem with orthogonality constraints.
method Sub-maximization problem with self-consistent-field (SCF) iteration for trace-fractional structure and orthogonal linear projections.
result Proposed algorithm converges globally to a KKT point and is more efficient.
Analyzed writing style changes in Danish high school students.
problem Detecting global development trends and identifying at-risk students in high school writing.
method Used a Siamese neural network to compute essay similarity and clustered student profiles.
result High school students' writing styles become less similar as they progress, with some students showing significant improvement and others limited development or setbacks.
New method disentangles style features from data augmentations.
problem Difficulty in deducing which data attributes are 'style' and should be discarded.
method Structured data augmentation with multiple style embedding spaces, maximizing joint entropy.
result Empirically demonstrates benefits on synthetic and real-world data.
New method predicts speaking style from text alone.
problem Predicting expressive speaking style from text.
method Text-Predicted Global Style Token (TP-GST) architecture.
result Synthesized speech has more pitch and energy variation.
Deep belief networks improve Dyna-style planning in large state spaces.
problem Lack of real data and difficulty in learning a good generative model for large state spaces.
method Used deep belief networks to learn an environment model for Dyna-style planning.
result Deep belief networks significantly outperform linear expectation models in empirical validation.
Generates outfits for e-commerce using neural networks.
problem Manual outfit creation by stylists is inefficient and not scalable.
method Multilayer neural network with visual and textual features.
result Generated outfits are preferred by users 21-34% more often.
End-to-end algorithm for W-2 distance using neural networks.
problem Training optimal transport mappings for W-2 distance.
method Input convex neural networks and cycle-consistency regularization.
result Algorithm scales well to high dimensions without bias.
Study develops supervised algorithm for symbolic music style translation.
problem Transforming musical style while maintaining original content.
method Synthetic data generation for aligned training data, encoder-decoder model.
result Trained models produce meaningful accompaniments for real MIDI recordings.