GPMM recovers latent signals from noisy mixtures using Bayesian inference.
problem Recovering latent signals from noisy mixed measurements.
method Gaussian process mixture of measurements (GPMM) with Bayesian inference.
result GPMM outperforms standard GP in signal recovery.
This work uses encoder-decoder networks to denoise one-dimensional signals by aligning clean and noisy signal latent representations.
problem Noise removal in one-dimensional signals, especially in medical and motion signals.
method Encoder-decoder architecture with adversarial learning to align clean and noisy signal latent representations.
result Better performance on electrocardiogram and motion signal denoising compared to learning-based and non-learning approaches.
VDA improves disentanglement of latent representations in complex signals.
problem Learning disentangled and interpretable representations in nonstationary, high-dimensional time-evolving signals.
method Variational decomposition autoencoding (VDA) framework, incorporating signal decomposition, contrastive self-supervised task, and variational prior approximation.
result DecVAEs surpass state-of-the-art VAE-based methods in disentanglement quality and generalization.
Develops a Bayesian non-parametric approach for signal separation with varying components.
problem Signal separation with varying components across different input locations.
method Augments Gaussian Process Latent Variable Models with weighted sums of pure component signals and incorporates priors for linear weights.
result Framework allows for non-linear variations in signals and incorporates useful priors for linear weights.
A new framework converts EEG signals between subjects and tasks.
problem Noise and variability in EEG data hinder generalizable signal extraction.
method Contrastive Split-Latent Permutation Autoencoder (CSLP-AE) framework.
result The CSLP-AE framework enables zero-shot conversion between unseen subjects.
The paper optimizes portfolios using MACD signals derived from price history.
problem Optimizing risky asset portfolios with latent mean-reverting and momentum factors.
method Derives optimal strategies based on MACD signals from EMA processes.
result Establishes admissibility and verification of optimal strategies.
Improved image reconstruction from sparse measurements using generative models.
problem Signal recovery from limited compressed measurements.
method Generative model with constrained latent variables for stable signal reconstruction.
result Improved reconstruction accuracy and preservation of realistic features.
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal. Therefore, (1) explaining away the structured noise in multiple-output regression is of paramount importanc…
We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent comp…
MultiImport infers node importance from multiple KG signals.
problem Inferring node importance in a knowledge graph from multiple input signals.
method End-to-end latent variable model using attentive graph neural networks.
result MultiImport consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100.
This paper uses RL and RNN to optimize trading with latent market information.
problem Optimal trading strategies exploiting latent information in financial markets.
method Blend of RL and RNN, integrating GRU networks for temporal dependencies.
result prob-DDPG achieves superior cumulative rewards and interpretable strategies.
Studying a softmax-attention model, we show that the learned query converges to the latent signal subspace spanned by the informative direction.
problem Understanding the theoretical principles of attention mechanisms in large-scale token collections.
method Deriving a population objective and analyzing the limiting ordinary differential equation of the learning dynamics.
result The learned query asymptotically recovers the latent signal up to the intrinsic sign ambiguity.
Estimates multi-attribute choice preferences using private signals and matrix factorization.
problem Modeling multi-attribute choice preferences under weak assumptions.
method Generative choice model with latent factor matrices and private signals; multi-stage matrix factorization.
result Validated estimation performance of novel algorithm through simulations.
New AMP algorithm estimates signals and latent variables in mixed regression models.
problem Estimating signals and latent variables in mixed regression models.
method Approximate Message Passing (AMP) algorithm for matrix GLM.
result State evolution recursion and optimal denoising functions for precise error minimization.
Model compresses event-like contexts using gated surprise signals.
problem Perceiving a dynamic world as organized events.
method Hierarchical, surprise-gated recurrent neural network architecture.
result Achieves best performance on multiple event processing tasks.
The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.
problem Limit order books can transition rapidly from stable to stressed conditions, making it difficult to detect the latent deterioration phase.
method The paper formalizes a three-regime causal data-generating process and proposes a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding.
result The proposed method achieves mean lead-time of +18.6 timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines.
EigenBayes: A fast, adaptive Bayesian shrinkage approach for high-dimensional matrix factorization
problem Choosing the latent dimension k in factor models method Adaptive spectral shrinkage and empirical Bayes calibration
result Adapts to signal-to-noise ratio and shrinks superfluous components
RISE framework unifies and improves time series learning with missing data.
problem Learning from time series with missing data.
method RISE framework unifies and improves time series learning with missing data.
result RISE instances always benefit from encoders that learn representations for numerical values.
New VAE limits latent layer information rate for better performance.
problem Improving latent layer information in VAEs.
method Imposes a signal-to-noise ratio on latent layer information.
result BIR-VAE provides meaningful latent representation with specified information rate.
Spectral method for detecting communities in time-varying networks from noisy signals.
problem Detect communities in time-varying networks from noisy signals.
method Spectral algorithm based on latent stochastic blockmodel.
result Consistent recovery of community structure in time-varying networks.
Improved robust latent variable estimation for neural dynamics.
problem Inconsistent results due to noise and nonlinearity in existing models.
method Probabilistic approach to latent variable estimation in decomposed models.
result More accurate latent variable inference in nonlinear systems with diverse noise conditions.
DFI maps covariates to latent representations for feature importance.
problem Feature importance when predictors are statistically dependent.
method Disentangled Feature Importance (DFI) using entropic optimal transport.
result DFI yields stable, interpretable, uncertainty-quantified attributions of shared predictive signal.
Autoencoder estimates parameters of noisy, multi-component damped signals.
problem Parameter estimation of damped sinusoidal signals under rapid decay and noise.
method Autoencoder-based approach using latent space for frequency, phase, decay, and amplitude estimation.
result High accuracy in parameter estimation, robustness to subdominant components and phase differences.
Decoupled PFNs improve sequential decision-making by separating epistemic and aleatoric uncertainties.
problem Sequential decision-making requires distinguishing between epistemic uncertainty about latent signals and irreducible aleatoric observation noise.
method Developed a decoupled PFN architecture that uses query-level labels to train separate heads for latent signal and aleatoric noise.
result Empirically, decoupled PFNs mitigate the failure mode of total-variance exploration in noisy and heteroscedastic settings.
The article develops models for learning and controlling physical systems with unknown inputs.
problem Learning and controlling physical systems with unknown inputs.
method Gaussian process latent force models (GP-LFMs) combining first-principles models and non-parametric GP components.
result Theoretical observability and controllability results for GP-LFMs.
Proposes using equivariant generative models for compressed sensing with unknown orientations.
problem Recovering signals with unknown orientations from underdetermined systems of linear measurements.
method Equivariant variational autoencoder as a generative prior for compressed sensing.
result Signals with unknown orientations can be recovered using iterative gradient descent on the latent space of equivariant models.
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion…
Estimates smooth graph signals from partial measurements.
problem Estimating latent signals on a graph from limited measurements.
method Smoothness penalized least squares estimator.
result Weak consistency for joint recovery of signals under stringent sampling.
LatentNN corrects neural network attenuation bias in astronomical data.
problem Neural networks underestimate extreme values due to measurement errors.
method Jointly optimizes network parameters and latent input values.
result LatentNN reduces attenuation bias across various signal-to-noise ratios.
Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous sp…
This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features o…
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 addresses converting speech to EGG signals without hardware, improving accuracy.
problem Estimating EGG signals from speech without hardware.
method Optimization of evidence lower bound with KL-divergence minimization.
result The method generates EGG signals that agree with gold standard and outperforms state-of-the-art.
GEM learns a manifold for cross-modal data, capturing structure without modality dependence.
problem Modality-specific neural models limit flexibility and custom architecture.
method Casts learning as manifold inference, enforcing coverage, linearity, and isometry.
result GEM learns latent structure across image, shape, audio, and cross-modal domains.
PCA outperforms random projections in retaining second order signals from latent groups.
problem Preserving second order structure in latent groups under unsupervised linear projections.
method Theoretical framework and quasi-exhaustive enumeration of projections.
result PCA outperforms random projections in retaining second order signals across a broad range of data-generating parameters.
A new model finds patterns enriched in target datasets.
problem Discovering patterns in datasets without labeled data.
method Probabilistic model for contrastive latent variable learning.
result Model recovers interesting structure in target dataset.
A new model family of zero-inflated Gaussian processes improves prediction and interpretability of rare event data.
problem Poor performance of conventional machine learning on zero-inflated datasets.
method Sparse kernels and latent probit Gaussian processes to zero out kernel rows and columns.
result Improves prediction of zero-inflated data and interpretability of latent mixing models.
AR-Flow VAE improves blind source separation with flexible autoregressive priors.
problem Unsupervised blind source separation of latent signals from mixtures.
method AR-Flow VAE uses autoregressive flows to model latent sources, enhancing flexibility and capturing complex dependencies.
result AR-Flow VAE effectively separates latent sources, demonstrating improved performance over conventional methods.
ATLAS separates invariant and transferable latent factors across diverse environments.
problem Transfer learning and robust prediction in heterogeneous environments.
method ATLAS leverages invariance principle to disentangle latent factors and uses auxiliary labels for robust prediction.
result Near-oracle performance and robust transferable prediction in new environments.
Neural responses are highly variable, and some portion of this variability arises from fluctuations in modulatory factors that alter their gain, such as adaptation, attention, arousal, expected or actual reward, emotion, and local metabolic resource availability. Regardless of their origin, fluctuations in these signal…
Develops methods to estimate high rank tensors from noisy data.
problem Estimating high rank tensors from noisy observations.
method Generative latent variable tensor model, polynomial-time spectral algorithm.
result Achieves computationally optimal rate for signal tensor estimation.
Proposes a new method for pattern localization in time series.
problem Locating a predefined sequence of patterns in a time series.
method Maps time series into a latent correlation space, then aligns them.
result Significant improvements over state-of-the-art methods.
The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the o…
ABACUS detects changes in multivariate data without labels.
problem Challenging to detect changes in correlated multivariate data.
method Bayesian source separation with multi-level sparsity.
result Competitive or superior performance in simulations and real-world applications.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
problem Improving single-cell RNA sequencing clustering by incorporating cell-cell communication.
method Integrates cell-cell communication into a variational autoencoder framework.
result Empirical results show CCCVAE outperforms standard VAEs in clustering performance.
Paper uses SGLD to recover signals from generative models, proving convergence under mild conditions.
problem Signal recovery from generative priors in compressed sensing.
method Stochastic Gradient Langevin Dynamics (SGLD) for signal recovery.
result SGLD converges to the true signal under mild assumptions on the generative model.
Half-AVAE enhances VAE for underdetermined ICA with adversarial training.
problem Challenges in ICA under underdetermined conditions.
method Encoder-free VAE with adversarial networks and EE terms.
result Half-AVAE outperforms baseline models in underdetermined ICA.
Paper analyzes optimal trading with latent factors causing price jumps and diffusion.
problem Optimal trading with latent factors affecting price movements.
method Demonstrates learning the posterior distribution over latent states and solving the optimal trading problem.
result Explicit solution to latent optimal trading problem and verification theorem.