SG-DNI outperforms standard neural interfaces in speed and accuracy.
problem Inflexible structure of neural networks limits parallelization.
method Introduces synthetic gradients with decoupled neural interfaces (SG-DNI).
result SG-DNI is over 3-fold faster with comparable accuracy.
PeL separates sensory interface optimization from decision learning.
problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.
Wield simplifies reinforcement learning task design.
problem Lack of suitable task representations for reinforcement learning.
method Introduces Wield, a system with software primitives and staged randomization protocol.
result Facilitates practical reinforcement learning through decoupling system and task design.
A new language for neural architecture search decouples search spaces and algorithms.
problem Current neural architecture search methods are limited to specific use-cases and lack general-purpose constructs.
method Proposes a formal language for encoding search spaces over general computational graphs, allowing modular, composable, and reusable encodings.
result The language enables easy experimentation with different search spaces and algorithms without reinventing the wheel.
Paper introduces HISA for efficient FHE computations.
problem Efficiently evaluating encrypted neural networks.
method Developed HISA for FHE applications, including compiler and runtime.
result Generated code is faster than hand-optimized implementations.
New method transfers between different modalities using latent spaces.
problem Cross-modal domain transfer between pretrained generative models.
method Learning a shared latent space between two generative models.
result Variational autoencoder best bridges latent spaces of different models.
A new framework decouples CNN features into intra-class and semantic differences.
problem Learning visual representations in CNNs is challenging.
method Proposes a decoupled learning framework that models intra-class variation and semantic difference independently.
result Decoupled reparameterization leads to significant performance gains and easier convergence.
Probabilistic decoupling separates labels from classes for improved classification.
problem Improving classification accuracy with noisy or partially labeled data.
method Probabilistic decoupling of labels from underlying classes.
result Method enhances performance on various classification tasks, including noisy and partially labeled data.
We investigate finite-time decoupled convergence in nonlinear two-time-scale stochastic approximation.
problem Achieving decoupled convergence in nonlinear two-time-scale stochastic approximation.
method Nested local linearity assumption, suitable step size selection, convergence analysis of matrix cross term, fourth-order moment convergence rates.
result Finite-time decoupled convergence rates can be achieved in nonlinear two-time-scale stochastic approximation with proper step size selection.
Study shows diffused interface flows to single diffused balls over time.
problem Volume-preserving mean curvature flow in Euclidean space.
method Diffused interface version, exponential convergence proof.
result Exponential convergence to single diffused balls.
Spectral decoupling improves neural network generalization in medical imaging.
problem Poor generalization of neural networks trained on medical imaging data.
method Spectral decoupling, a regularization technique that encourages learning more features.
result Spectral decoupling increases network robustness and performance on external datasets.
Decoupled GCN is shown to be equivalent to label propagation.
problem Improving semi-supervised node classification in graph learning.
method The paper proves the equivalence of decoupled GCN and label propagation, and proposes a new method named PTA.
result Decoupled GCN is equivalent to two-step label propagation and can automatically assign weights to pseudo-labels.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inferenc…
Develops a 2D Allen-Cahn theory for interfaces with boundary.
problem Analyzing interfaces with boundary in the line-bundle framework.
method Constructs a model solution on the punctured plane and develops its stability and invertibility theory.
result Constructs Allen-Cahn sections concentrating on prescribed line segments.
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in H23k+1 and the velocity fields in H23k. These estimates are obtained using geometric considerations which show th…
Study boundary behavior of limit interfaces in Riemannian manifolds without convexity assumptions.
problem Boundary behavior of limit interfaces in Riemannian manifolds.
method Proves limit-interface is a free boundary varifold, integer rectifiable up to boundary.
result No convexity assumption required; valid even when limit-interface clusters near boundary.
Deep learning predicts curvature of 2D interfaces in level-set method.
problem Estimating curvature in level-set method for complex interfaces.
method Deep learning using feed-forward neural networks trained on synthetic data.
result Deep learning models approximate curvature with comparable precision to traditional methods.
sktime provides a unified interface for time series machine learning.
problem Handling diverse time series learning tasks.
method Unified API for scikit-learn compatible time series tasks.
result Unified approach simplifies time series machine learning.
Interactive interface for specifying complex tasks through demonstrations.
problem Challenges in specifying high-dimensional reward functions for complex tasks.
method Interactive interface with a set of increasingly complex policies trained on demonstrations.
result Successfully learned a complex task (Lunar Lander) with interactive demonstrations.
Proposes new feature transformation methods for brain interface models.
problem Sub-optimality of feature ranking and selection in brain interface models.
method Introduces maximum mutual information linear and nonlinear transformations.
result Significantly better performance in binary and multi-class decoding analyses.
The paper proposes a decoupled approach to efficiently estimate CoVaR, a measure of systemic financial risk.
problem Estimating CoVaR, a measure of systemic financial risk, is challenging due to zero-probability events and portfolio repricing.
method The paper introduces a decoupled approach using smoothing techniques and a functional perspective to model CoVaR.
result The decoupled estimator achieves a rate of convergence of approximately OmP(Γ−1/2). Study on the regularity of p-Gauss curvature flow near flat interfaces.
problem Regularity of p-Gauss curvature flow near flat interfaces. method Analysis of convex hypersurface near the interface.
result Regularity of the convex hypersurface near the interface.
Analytic patch trees reveal new geometric structures and dimension fields.
problem Understanding the geometric and analytical properties of surface patch trees.
method Developed analytic surface patch trees and introduced interface curves to transmit state.
result Surface patch trees have natural foliations with one-dimensional curve trees and dimension fields.
Decouples RL components for better task transfer and robustness.
problem Poor generalization in reinforcement learning to task domain changes.
method Separates learning into task representation, dynamics, and reward.
result Decoupling improves performance and robustness to domain changes.
New BMI interface stabilizes user experience over long periods.
problem Inconsistent neural signal data leads to interface recalibration.
method Adversarial Domain Adaptation Network to match residual distributions.
result Adversarial Domain Adaptation Network outperforms other methods.
This paper investigates the effectiveness of decoupled weight decay at the start of training.
problem The traditional approach to weight decay is not effective throughout training.
method The authors investigate decoupled weight decay, applying it only at the start of training.
result Applying weight decay only at the start of training stabilizes network weights and improves performance.
Diffuse interface methods have recently been introduced for the task of semi-supervised learning. The underlying model is well-known in materials science but was extended to graphs using a Ginzburg--Landau functional and the graph Laplacian. We here generalize the previously proposed model by a non-smooth potential fun…
Clarifies method of phase synchronization for decoupling linear differential equations.
problem Velocity-dependent transformations in linear second-order differential equations.
method Linear transformation of coordinates and velocities.
result Velocity-dependent transformations do not preserve second-order character and define their own system.
Agents learn user preferences with less explicit feedback via spatial interface valuing.
problem Learning user preferences with high cognitive load feedback.
method Spatial Interface Valuing for reduced explicit feedback.
result Agents learn faster with spatial interface valuing compared to explicit feedback.
Common models for two-phase lipid bilayer membranes are based on an energy that consists of an elastic term for each lipid phase and a line energy at interfaces. Although such an energy controls only the length of interfaces, the membrane surface is usually assumed to be at least C1 across phase boundaries. We consi…
Kahler geometry explains decoupling of Kerr perturbations.
problem Decoupling of curvature scalars in Kerr spacetime.
method Hidden Kahler structure in Kerr spacetime, showing decoupling as a consequence of Kahler geometry.
result Decoupling of Teukolsky equations on Kahler background.
Generative adversarial networks improve brain-computer interface performance with limited data.
problem Limited training samples in brain-computer interfaces.
method Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for data augmentation.
result Generated artificial EEG data improves classification accuracy in brain-computer interface tasks.
Improved node classification in signed social networks using diffuse interface methods.
problem Classifying nodes in signed social networks (positive and negative interactions).
method Diffuse interface methods based on Ginzburg-Landau functional and extended graph Laplacian.
result Performance improvement in real signed social networks, outperforming state of the art.
Study evaluates features and classifiers for brain-computer interface tasks.
problem Improving accuracy in brain-computer interface communication.
method Examined six classical features and twelve classifiers across nine datasets.
result Energy in α and η bands, and Bayesian classifier with Gaussian assumption, outperform other methods.
AdaDEM decouples EM into two parts to improve class overlap and uncertainty.
problem Improper EM limits its effectiveness in various machine learning tasks.
method Decouple EM into CADF and GMC, and AdaDEM normalizes CADF reward and uses MEC.
result AdaDEM outperforms classical EM and improves performance in noisy and dynamic environments.
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
problem Reducing the cost of user interface experimentation in online retail.
method Modeling user interface experimentation as an opportunistic bandit problem, reducing the cost of exploration.
result Significant regret reduction and improved contextual information for testing.
Recent variants improve knowledge distillation performance.
problem Improving the performance of knowledge distillation.
method Introducing additional components or changing the learning process.
result These variants have shown promising results.
New algorithms for interpreting complex multivariate functions.
problem Hard interpretation of multivariate functions due to many parameters.
method Filtered tensor decompositions of derivative information.
result Nonparametric estimates of smooth decoupled functions.
Study motion of discrete interfaces on triangular lattice using Almgren, Taylor, and Wang's approach.
problem Motion of discrete interfaces on triangular lattice driven by ferromagnetic interactions.
method Coupling Almgren, Taylor, and Wang's minimizing movements approach with Braides, Gelli, and Novaga's discrete-to-continuum analysis.
result Limit motion of origin-symmetric convex hexagons compared to crystalline curvature evolution.
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal …
New method combines spectral and sparse methods for Gaussian processes.
problem Efficiently fitting Gaussian processes to large datasets.
method Orthogonally decoupled variational Fourier features.
result Competitive performance on synthetic and real-world data.
Study on evolving interfaces with complex curvature and density effects.
problem Understanding the dynamics of evolving heterogeneous elastic interfaces.
method Modeling an evolving curve with a density function, analyzing the associated gradient flow evolution.
result Analysis of the preservation and asymptotic behavior of geometric properties in the evolving system.
We establish decoupled functional CLTs for two-time-scale stochastic approximation.
problem Understanding the asymptotic behavior of two-time-scale stochastic approximation.
method Martingale problem approach and auxiliary sequence.
result The limiting dynamics of two-time-scale SA are independent of each other.
Study on liquid-vapor interfaces in stable equilibrium without assuming prior regularity.
problem Understanding the conditions for a liquid-vapor interface to be in stable equilibrium without assuming prior regularity.
method Proposes a weakest set of mathematical assumptions using varifold regularity theory and identifies a suitable stability condition.
result The liquid-vapor interface is a smoothly embedded analytic surface in stable equilibrium.
GenSBI offers JAX-native SBI methods for natural sciences.
problem Lack of native SBI libraries in JAX for natural sciences.
method Flow and diffusion generative models in JAX.
result Near-ideal mean C2ST scores on SBIBM tasks.
Study modular concept learning with different oracle interfaces.
problem Learning a concept that is a cross product of component concepts.
method Analyze different types of oracle interfaces and queries.
result Modular concept learning is easier with positive examples and membership queries.