Paper introduces probabilistic module interface for complex models and inference.
problem Handling complex probabilistic models with latent variables and custom inference methods.
method Develops a platform-agnostic interface for encapsulating models and inference programs, allowing sound approximate inference algorithms for networks of modules.
result Sound approximate inference algorithms can be constructed for networks of probabilistic modules.
New scheme optimizes BMI through probabilistic and geometric shaping.
problem Optimizing bit-wise mutual information (BMI) for coded modulation.
method Joint optimization of BMI through probabilistic and geometric shaping.
result Joint optimization enables a continuum of constellation geometries and probability distributions.
PICLE uses probabilistic models to efficiently evaluate and compose modules for continual learning.
problem Challenging search space of module compositions in continual learning.
method Probabilistic framework to cheaply compute module compositions' fitness.
result First modular CL algorithm to achieve perceptual, few-shot, and latent transfer.
Framework predicts interactions between multiple traffic participants.
problem Accurately predicting interactions between multiple traffic participants.
method Probabilistic framework with hierarchical modules forecasting intentions and motions.
result Framework predicts continuous motions and interaction durations for multiple interacting road participants.
Proposes GPCA module for channel attention in CNNs using Gaussian processes.
problem Improving performance in visual tasks through effective channel selection.
method Integrates Gaussian processes into channel attention mechanisms for probabilistic modeling of channel correlations.
result Demonstrates improved performance of GPCA module in end-to-end CNN training.
New algorithm for contextual combinatorial bandits with probabilistic arm triggering.
problem Optimizing decisions in dynamic environments with probabilistic arm availability.
method C^2-UCB-T and VAC^2-UCB algorithms with TPM and VM conditions.
result Achieved improved regret bounds for contextual combinatorial bandits.
ProbRes calibrates probabilistic forecasts by learning volatility dynamics.
problem Quantifying risk and uncertainty in time series forecasting.
method ProbRes learns conditional mean and volatility separately, generating well-calibrated prediction intervals.
result ProbRes accurately captures predictive distributions and produces well-calibrated prediction intervals.
ScoreGrad predicts multivariate time series with energy-based models, achieving state-of-the-art results.
problem Predicting multivariate time series with generative models while considering noise and distribution.
method ScoreGrad uses continuous energy-based generative models with a feature extraction and score matching module.
result ScoreGrad achieves state-of-the-art results on six real-world datasets.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
Bayesian Layers adds uncertainty to neural networks, enabling faster experimentation and scalability.
problem Enabling neural networks to quantify uncertainty in predictions.
method Drop-in replacements for common layers, capturing uncertainty over weights, activations, etc.
result Bayesian Layers can fit large models like 5-billion parameter Bayesian Transformers.
Autoencoders improve communication system performance by optimizing constellation geometry and probability.
problem Optimizing constellation geometry and probability for better communication system performance.
method Leveraging autoencoders to learn capacity-achieving symbol distributions and constellations.
result Learned constellations achieve information rates very close to capacity on AWGN channels and outperform existing methods on fading channels.
Develops a neural framework for probabilistic forecasting of dynamical systems.
problem Uncertainty quantification in dynamical systems using trajectory-oriented approaches.
method D2D neural probabilistic forecasting framework using kernel mean embeddings and mixture density networks.
result The D2D model captures distributional evolution in chaotic systems and produces skillful probabilistic forecasts.
Framework tracks and predicts multiple objects in autonomous driving.
problem Challenges in multi-target tracking due to object number fluctuation and occlusion.
method Constrained mixture sequential Monte Carlo (CMSMC) method with a mixture representation.
result Framework can track and predict multiple objects simultaneously without explicit data association.
Improved regret bounds for combinatorial semi-bandits with probabilistically triggered arms.
problem Regret bounds containing an exponentially large factor of 1/p* in prior studies.
method Introducing a TPM bounded smoothness condition to remove the factor of 1/p*.
result Significantly better regret bounds for influence maximization and cascading bandits.
Improved community detection in graphs with probabilistic models.
problem Lack of probabilistic formulation and fixed number of communities in GNN-based methods.
method Combines GNNs with amortized clustering for variable numbers of clusters.
result Improved performance on synthetic and real datasets compared to previous methods.
VIR model improves regression accuracy and uncertainty estimation for imbalanced data.
problem Imbalanced regression datasets lead to poor model accuracy and uncertainty estimation.
method VIR model uses probabilistic smoothing and reweighting to estimate latent representations and uncertainty.
result VIR model outperforms state-of-the-art models in accuracy and uncertainty estimation.
Study improves seasonal forecasts using deep learning.
problem Challenges in generating large forecast ensembles and limited observations for verification.
method Developed a probabilistic deep neural network model.
result Demonstrated favorable skill compared to state-of-the-art dynamical forecast systems.
New grammar model learns sentence structure with latent variables.
problem Grammar induction for sentences with complex dependencies.
method Compound probabilistic context-free grammar with latent variables, variational inference.
result Effective unsupervised parsing compared to state-of-the-art methods.
Prob-GNN quantifies travel demand uncertainty with deep learning.
problem Uncertainty in travel demand prediction.
method Probabilistic Graph Neural Networks (Prob-GNN) framework.
result Probabilistic assumptions significantly impact uncertainty prediction.
New method learns belief representations for GAIL in POMDPs.
problem Imitation learning in partially observable Markov decision processes (POMDPs).
method Joint learning of belief module and policy with task-aware imitation loss and belief regularization.
result Our BMIL approach outperforms GAIL and task-agnostic belief learning.
System predicts vehicle interactions and trajectories with uncertainty.
problem Predicting future vehicle trajectories with uncertainty.
method Hierarchical Bayesian Generative Modeling with categorized and real-valued coordination variables.
result Categorized coordination better captures multi-modality and generates more diverse samples.
This paper introduces the class of volatility modulated Lévy-driven Volterra (VMLV) processes and their important subclass of Lévy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot prices can be modelled di…
This paper includes a proof of well-posedness of an initial-boundary value problem involving a system of degenerate non-local parabolic PDE which naturally arises in the study of derivative pricing in a generalized market model. In a semi-Markov modulated GBM model the locally risk minimizing price function satisfies a…
This article studies a portfolio optimization problem, where the market consisting of several stocks is modeled by a multi-dimensional jump-diffusion process with age-dependent semi-Markov modulated coefficients. We study risk sensitive portfolio optimization on the finite time horizon. We study the problem by using a …
New method learns PDE solutions from low-fidelity data.
problem Challenges in learning PDE surrogates with scarce data.
method Flow matching in infinite-dimensional space with conditional neural operators.
result Accurately learns PDE solutions across different resolutions and fidelities.
Proposes a method to optimize neural network structures and parameters.
problem Challenges in selecting and designing neural network structures.
method Probabilistic modeling to generate network structures and optimize their parameters.
result The method can find appropriate and competitive network structures.
Paper generalizes kernel mean embedding to von Neumann-algebra-valued measures.
problem Analyzing complex multivariate distributions and quantum mechanics.
method Generalizes kernel mean embedding to von Neumann-algebra-valued measures in reproducing kernel Hilbert modules.
result Injectivity and universality of the generalized KME are confirmed.
New framework integrates SPNs with weighted model integration for hybrid data.
problem Handling mixed discrete-continuous data with tractable probabilistic representations.
method Sum-Product Networks (SPNs) with weighted model integration for hybrid domains.
result Effective framework for conditioning on interval constraints in hybrid data.
Develops a deep model for joint image-text learning.
problem Bidirectional joint image-text modeling.
method Variational hetero-encoder randomized GAN (VHE-GAN).
result Achieves state-of-the-art performance in image-text learning and generation.
A new quandle from link modules helps identify link properties.
problem Identifying link properties from their modules.
method Defining quandle operations on multivariate Alexander modules.
result The fundamental multivariate Alexander quandle determines the link module sequence.
Classifies modules of surface-knots in terms of their properties.
problem Characterizing modules of surface-knots in terms of their properties.
method Using homology and covering spaces, the reduced first module is characterized.
result The reduced first module for every genus g is characterized in terms of properties of a finitely generated module.
Bayesian priors and penalties are equivalent in variational inference.
problem Understanding the relationship between Bayesian priors and penalties in variational inference.
method Characterizing the regularizers that can arise in variational inference and providing a systematic way to compute the prior corresponding to a given penalty.
result Equivalence between Bayesian priors and penalties in variational inference.
New method learns both module structure and sequencing in neural networks.
problem Learning only the parameters and order of execution of neural modules.
method Expands the approach to learn the internal structure of modules, including the ordering and combination of arithmetic operators.
result Performance comparable to hand-designed modules achieved without extra supervisory signals.
Method predicts spinal deformity progression using 3D models and machine learning.
problem Predicting the progression of spinal deformities in scoliosis patients.
method Discriminative probabilistic manifold embedding for 3D spine models.
result 81% classification rate and 2.1° prediction difference in curve angulation.
The document provides tables of prehomogeneous and étale modules for reductive algebraic groups.
problem Classifying and tabulating prehomogeneous and étale modules for reductive algebraic groups.
method Classification and tabulation of prehomogeneous and étale modules based on existing work and the author's determination.
result Tables of prehomogeneous and étale modules for reductive algebraic groups with up to two simple factors.
Curvature defined for Hilbert modules and Kasparov modules.
problem Defining and studying curvature in Hilbert modules and Kasparov modules.
method Introduced curvature for densely defined universal connections on Hilbert C∗-modules relative to spectral triples. result Curvature only depends on the represented form of the universal connection modulo junk forms.
HMMs improve music transcription accuracy.
problem Improving automatic transcription of music.
method Employed PLCA for multi-pitch estimation and integrated HMMs for note segmentation and post-processing.
result HMMs enhance transcription accuracy on different instruments.
Proves finiteness and holonomicity of skein modules for 3-manifolds.
problem Finiteness and holonomicity of skein modules for 3-manifolds.
method Defining skein transfer bimodules and using q-analogues of D-module theory.
result Internal skein modules are holonomic modules over the internal skein algebra of the boundary.
Defines super projective modules and explores their properties.
problem Exploring the geometric-algebraic link in super geometry.
method Defined and explored super projective modules over supersmooth functions.
result Module of vector fields over a supersphere is a super projective module.
Corrects approximate Bayesian inference for better decision-making.
problem Sub-optimal decisions due to inaccurate posterior predictive distributions.
method Trains a separate model to correct decision-making under approximate posterior, combining Bayesian modeling with optimization.
result Empirically demonstrates improved predictive accuracy in various problems.
A new method to derive presentations of skein modules is developed. For the case of homotopy skein modules it will be shown how the topology of a 3-manifold is reflected in the structure of the module. The freeness problem for q-homotopy skein modules is solved, and a natural skein module related to linking numbers is …
We introduce higher skein modules of links generalizing the Conway skein module. We show that these modules are closely connected to the HOMFLY polynomial.
Robot framework identifies unknown objects from symbolic descriptions.
problem Processing unmodeled objects in unstructured environments.
method Discriminative probabilistic model interpreting symbolic descriptions.
result Improved identification performance through ensemble learning.
Paper compares skein modules to Kauffman bracket modules.
problem Comparing skein modules to Kauffman bracket modules.
method Using skein relations and Reshetikhin-Turaev model.
result Resolved the problem of comparing skein modules to Kauffman bracket modules.
Skein modules are the main objects of an algebraic topology based on knots (or position). In the same spirit as Leibniz we would call our approach "algebra situs." When looking at the panorama of skein modules we see, past the rolling hills of homologies and homotopies, distant mountains - the Kauffman bracket skein mo…
New knot invariant detects unknots.
problem Detecting unknots in knot theory.
method Introduced Alexander-Beck module as a refined Alexander module.
result Alexander-Beck module detects the unknot.
Generalized Steinberg module presentation for Gaussian and Eisenstein integers.
problem Presenting Steinberg modules for specific number rings.
method Generalization of Bykovskii's presentation to Gaussian and Eisenstein integers.
result Generalization does not yield a presentation for all Euclidean number rings.
Enhanced Alexander module detects linking numbers in links.
problem Detecting linking numbers in links using Alexander modules.
method Defining and singling out meridians and longitudes in reduced Alexander modules.
result The enhanced Alexander module determines all linking numbers.