Enhances spoken speech quality using EEG signals.
problem Improves speech clarity in noisy environments.
method Generative adversarial network (GAN), gated recurrent unit (GRU), temporal convolutional network (TCN) regression models.
result Significant improvement in speech enhancement quality compared to traditional methods.
Enhances speech recognition in new environments by embedding noise and scaling training data.
problem Improving speech recognition in unseen noisy environments.
method Embedding noise from unseen environments and scaling training data to 16,784 environments.
result Reduced word error rate from 34.04% to 15.46% on enhanced speech.
This work improves speech enhancement using multiple GAN generators in stages.
problem Efficient speech enhancement using GANs with multiple stages.
method Multiple GAN generators in a chained multi-stage approach, with two scenarios: shared or independent parameters.
result Multi-stage SEGAN outperforms one-stage SEGAN, with independent generators yielding better results.
Enhancement attacks can falsely improve machine learning model performance in biomedical research.
problem The trustworthiness of machine learning in biomedical research is threatened by enhancement attacks.
method Developed two techniques to enhance prediction performance with minimal changes to features.
result Falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities.
Enhances psyquandle counting invariants using cocycles.
problem Improving the counting invariant of pseudoknots and singular knots.
method Defining enhancements via biquandle 2-cocycles and new functions.
result New polynomial invariants that are proper enhancements and not determined by the Jablan polynomial.
Enhances speech from reverberated and noisy signals using Wide Residual Networks.
problem Improving intelligibility of speech from reverberated and noisy signals.
method Wide Residual Network architecture with residual connections for processing contextually correlated representations.
result Enhanced speech from the proposed method succeeded for both enhancement and speech recognition tasks.
Paper introduces DACAL for high-resolution photo and video enhancement.
problem Photo and video enhancement with weak supervision.
method Divide-and-conquer adversarial learning approach with hierarchical decomposition.
result State-of-the-art performance in high-resolution photo and video enhancement.
An enhanced trivalent tangle is a trivalent tangle with some of its edges labeled. We use enhanced trivalent tangles and classical knot theory to provide a recipe for constructing invariants for trivalent tangles, and in particular, for knotted trivalent graphs. Our method also yields invariants of, what we refer to as…
Enhanced Teichmüller space for surfaces with decorations and enhancements.
problem Parameterizing and understanding Teichmüller spaces with enhancements and decorations.
method Introduced a new variation of Teichmüller space, constructed parameterization, and introduced lamination space.
result Compatibility of shear coordinates and λ-length coordinates in the new deformation space.
siRF identifies transcription factor binding near enhancers in flies.
problem Identifying functional transcription factor binding near enhancers.
method Signed iterative random forests (siRF) for machine learning.
result Infers regulatory interactions among transcription factors and enhancers.
We enhance the biquandle counting invariant using elements of truncated biquandle-labeled Polyak algebras. These finite type enhancements reduce to the finite type enhancements defined by Goussarov, Polyak and Viro for the trivial biquandle of one element and determine (but are not determined by) the biquandle counting…
Enhances knot invariants using bilinear forms on vector spaces.
problem Improving classical and virtual knot invariants.
method Uses bilinear forms on vector spaces indexed by pairs of elements of a finite quandle.
result New enhanced invariants of knots and links.
Enhanced indexation uses equity and index options for better performance.
problem Improving portfolio performance through enhanced indexation.
method Integrating index options into an enhanced indexation strategy based on second-order stochastic dominance.
result Introducing option strategies in enhanced indexation leads to improved out-of-sample performance.
Quantum polynomials are derived from a specific tribracket structure.
problem Quantum enhancement polynomials for oriented links.
method Defined using a canonical two-element tribracket, proving polynomials can be derived from five specific ones.
result Universal quantum enhancement polynomials are strictly stronger than the Jones polynomial.
Enhances neural networks' robustness against adversarial samples without sacrificing clean sample generalization.
problem Limited generalization and time complexity of adversarial training.
method Feature Pyramid Decoder (FPD) framework that integrates denoising and image restoration modules into CNNs and constrains the Lipschitz constant.
result FPD-enhanced CNNs achieve sufficient robustness against general adversarial samples on various datasets.
Efficient trainable front-end for neural speech enhancement.
problem Inefficient STFT front-ends in neural speech enhancement models.
method Butterfly mechanism for Fast Fourier Transform, trainable STFT window.
result Accuracy and efficiency improvements for low-compute systems.
We consider involutory virtual biracks with good involutions, also known as symmetric involutory virtual biracks. Any good involution on an involutory virtual birack defines an enhancement of the counting invariant. We provide examples demonstrating that the enhancement is stronger than the unenhanced counting invarian…
Proposes AWS method for precise speech enhancement using DNN.
problem T-F resolution problem in fixed-resolution short-time frequency transforms.
method Incorporates trainable adaptive window switching into speech enhancement procedure.
result Achieved higher signal-to-distortion ratio than conventional methods.
Enhances knot counting invariant using skew braces.
problem Counting invariant for virtual knots and links.
method Introduces new invariants using skew brace structures.
result New invariants not determined by the counting invariant.
We define enhancements of the quandle counting invariant for knots and links with a finite labeling quandle Q embedded in the quandle of units of a Lie algebra \mathfrak{a} using Lie ideals. We provide examples demonstrating that the enhancement is stronger than the associated unenhanced counting invariant.
Enhanced rotation prediction improves SSL models by capturing both shape and texture information.
problem Rotation prediction misses texture information, limiting model performance.
method Introduces image enhanced rotation prediction (IE-Rot) that combines rotation and image enhancement tasks.
result IE-Rot models outperform Rotation on various benchmarks.
Speech enhancement improved by adapting to unknown speakers without auxiliary signals.
problem Improving speech enhancement accuracy for unknown speakers.
method Adopting multi-task learning for speech enhancement and speaker identification, using multi-head self-attention.
result Achieved state-of-the-art performance and improved subjective quality.
Diffusion models enhance speech without supervision.
problem Challenges in generalizing supervised speech enhancement methods to unseen conditions.
method Unsupervised speech enhancement using diffusion-based generative models.
result Demonstrates promising results compared to supervised and unsupervised baselines.
RDL-Net improves speech enhancement with fewer parameters and better performance.
problem Improving speech enhancement with fewer parameters and better performance.
method Proposes RDL-Net, a CNN combining residual and dense aggregations without over-allocating parameters.
result RDL-Net achieves higher speech enhancement performance with fewer parameters and lower computational requirements.
Enhances knotoid counting invariants using biquandle brackets.
problem Counting and distinguishing knots and links.
method Defines biquandle brackets with skein relations and coefficients, enhancing biquandle counting matrix invariant.
result New invariants are stronger than previous ones.
Improved speech enhancement with larger neural networks using novel embeddings and biases.
problem Decreased robustness of speech enhancement models to real-world use cases.
method Frequency-positional embeddings, semi-supervised training, biased loss function.
result Better performance on real recordings with improved large neural network architecture.
Enhanced Yang-Baxter operators give rise to invariants of oriented links. We expand the enhancing method to generalized Yang-Baxter operators. At present two examples of generalized Yang-Baxter operators are known and recently three types of variations for one of these were discovered. We present the definition of enha…
Enhances labels from unlabeled data using sample correlations.
problem Lack of label distributions in real-world applications.
method Proposes LESC and gLESC methods to enhance label distributions.
result Improves performance of label enhancement through sample correlations.
Speech enhancement improved using variational autoencoders and alpha-stable noise models.
problem Improving speech quality in noisy environments.
method Deep generative model using variational autoencoders with alpha-stable noise model.
result The proposed method outperforms conventional approaches in speech intelligibility and quality.
Combinatorial proof of grid homology properties.
problem Properties of double-point enhanced grid homology.
method Purely combinatorial proof, extended to Z \mathbb{Z} Z coefficients. result Skein exact sequence obeyed by grid homology.
Noise enhancement improves generalization in training.
problem Improving generalization in training with controlled noise.
method Noise enhancement method to control SGD noise without changing learning rate or minibatch size.
result Noise enhancement improves generalization for real datasets.
Paper proposes a method to enhance low-quality retinal images using optimal transport.
problem Artifacts and imperfections in retinal images lead to diagnostic inaccuracies.
method Leveraging optimal transport theory, an unpaired image-to-image translation scheme is proposed.
result The method improves perceptually and quantitatively the quality of low-quality retinal images.
Improved speech enhancement using diffusion models with MSE loss.
problem Efficient incorporation of noisy speech in generative speech enhancement.
method Augmented diffusion-based generative model with a MSE loss for enhanced speech.
result Proposed method improves speech enhancement performance compared to original diffusion model.
Enhances image quality to improve test-time adaptation accuracy.
problem Reducing accuracy loss due to distribution shift in deep networks.
method Integrates image enhancement with TTA methods to reduce prediction uncertainty.
result TECA method increases accuracy of TTA methods without hyperparameters.
In this paper, we consider biquandle colorings for knotoids in R 2 \mathbb{R}^2 R 2 or S 2 S^2 S 2 and we construct several coloring invariants for knotoids derived as enhancements of the biquandle counting invariant. We first enhance the biquandle counting invariant by using a matrix constructed by utilizing the orientation a kno…
This paper tackles unsupervised speech enhancement using RVAE and proposes efficient sampling methods.
problem Unsupervised speech enhancement with high computational complexity.
method Recurrent variational autoencoder (RVAE) combined with Langevin dynamics and Metropolis-Hasting sampling.
result Sampling-based algorithms outperform VEM and achieve robust generalization.
Gradient-enhanced deep GPs improve multifidelity model accuracy.
problem Improving accuracy in multifidelity models using gradient data.
method Extending deep Gaussian processes to incorporate gradient data.
result Gradient-enhanced deep GP outperforms other models in predicting aerodynamic coefficients.
Enhances projection pursuit tree classifier with visual diagnostics for better multi-class classification.
problem Rigidity of original algorithm limits performance in complex high-dimensional classification problems.
method Allowing more splits and flexible class groupings in projection pursuit computation, and developing visual diagnostics.
result Demonstrates enhanced classifier performs as intended through interactive visual diagnostics.
The paper analyzes how speech enhancement and recognition can be improved in noisy environments.
problem Improving speech recognition in multi-talker scenarios with limited resources.
method Developed and trained two LSTM-based models for speech enhancement and phone recognition, then studied their joint optimization.
result Joint optimization of speech enhancement and recognition leads to a significant reduction in Phone Error Rate (PER).
Enhanced VAE with DT improves flexibility in latent variable modeling.
problem Limitations of VAE's diagonal covariance matrix in matching true posterior distribution.
method Proposes dyadic transformation (DT) to model multivariate normal distributions.
result DT enhances posterior flexibility and achieves competitive results.
Improved electrical load forecasting model using Fourier-enhanced RNN.
problem Electrical load time series downscaling with high accuracy and low error.
method Combines recurrent neural network with Fourier seasonal embeddings and self-attention.
result Significantly reduces RMSE across different time horizons compared to existing methods.
Enhances quantum invariants using tribracket brackets.
problem Quantum invariants of tribracket-colored knots and links.
method Introduces tribracket brackets as skein invariants.
result Provides new quantum invariants and examples.
EdgeFool generates adversarial images to mislead classifiers.
problem Misleading classifiers with adversarial images.
method Trains a fully convolutional neural network to generate perturbations that enhance image details and mislead classifiers.
result EdgeFool outperforms other adversarial methods on various classifiers and datasets.
Enhances Hantzsche's theorem for 3-manifolds in 4D.
problem Embedding 3-manifolds in 4-dimensional space.
method Using Heegaard diagrams and a property called doubly unlinked (DU).
result Enhanced Hantzsche's embedding obstruction.
Enhanced DQN model boosts trading performance with advanced techniques.
problem Improving automated trading performance in financial markets.
method Incorporation of Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, Double DQN, and CNN architectures.
result Significantly improved returns and Sharpe Ratio compared to the original DQN model.
In a previous paper, the authors defined an equivariant version of the so-called Saito duality between the monodromy zeta functions as a sort of Fourier transform between the Burnside rings of an abelian group and of its group of characters. Here a so-called enhanced Burnside ring B ^ ( G ) \widehat{B}(G) B ( G ) of a finite group G G G …
Unified framework for subgraph-enhanced GNNs, improving prediction accuracy and reducing computation time.
problem Limited understanding of subgraph-enhanced GNNs and their relation to the Weisfeiler-Leman hierarchy.
method Theoretical framework, theoretical expressivity results, and data-driven subgraph sampling methods.
result Data-driven subgraph-enhanced GNNs outperform non-data-driven methods in predictive performance.
Study shows specific states produce Khovanov homology torsion.
problem Understanding torsion in Khovanov homology.
method Proved specific sum of enhanced states produce torsion.
result Proved specific states produce Khovanov homology torsion of order two.