A novel circuit motif uses sister cells for inference with correlated priors.
problem Structured priors in neural systems pose architectural challenges.
method Proposes a novel circuit motif using sister cells to implement correlated priors without direct interactions.
result Demonstrates the efficacy of correlated priors for inference in noisy environments.
We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated U…
Unified analysis of privacy leakage in correlated data considering prior knowledge.
problem Understanding the impact of prior knowledge and data correlation on privacy leakage.
method Proposed prior differential privacy (PDP) and analyzed using WHG and multivariate Gaussian models.
result Derived closed-form expression for continuous data and chain rule for discrete data.
ACVAEs improve on CVAEs by learning more flexible latent correlations.
problem Learning latent representations with correlated structure.
method Adaptive prior distribution and belief propagation.
result ACVAEs outperform CVAEs in link prediction and hierarchical clustering.
This paper introduces hierarchical Gaussian process priors for neural networks to capture weight correlations and inductive biases.
problem Capturing weight correlations and inductive biases in neural networks.
method Hierarchical Gaussian process priors with unit embeddings and input-dependent kernels.
result Hierarchical Gaussian process priors provide competitive predictive performance and desirable uncertainty estimates.
Bayesian priors for neural networks are improved by incorporating weight correlations and tail behavior.
problem Improving Bayesian priors for neural networks to better reflect true beliefs and performance.
method Analyzed summary statistics of neural network weights in different architectures and incorporated these observations into new priors.
result Improved performance on image classification datasets by using new priors that account for weight correlations and tail behavior.
Richer prior for neural networks with correlated weights.
problem Weak priors limit neural network complexity and flexibility.
method Latent variables represent network units, conditional weights.
result Richer meta-representations and representations.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
Weak diffusion priors can still perform well in inverse problems.
problem Using mismatched or low-fidelity diffusion priors in inverse problems.
method Extensive experiments and theoretical analysis combining Bayesian-consistency theory and local-correlation analysis.
result Weak priors succeed when measurements are highly informative, and they fail in other regimes.
tvGP-VAE models tensor-valued latent variables with Gaussian processes for better data structure representation.
problem Agnostic latent variables in VAEs ignore data structure correlations.
method Proposes tensor-variate Gaussian process prior for variational autoencoder.
result Explicitly modeling correlation structures improves model performance in reconstruction.
CVAEs improve VAEs by accounting for correlations in latent representations.
problem VAEs fail to account for correlations between data points, limiting their effectiveness.
method CVAEs incorporate correlation structure into VAEs using a prior and tractable approximations.
result CVAEs outperform baseline algorithms in matching and link prediction tasks.
Bayesian method for dynamic correlation matrices improves accuracy and responsiveness.
problem Challenges in estimating time-varying correlation matrices, including slow adaptation, insufficient regularization, and diffuse uncertainty.
method Low-rank factor representation with dynamic shrinkage prior and multivariate factor stochastic volatility model.
result Improved accuracy and responsiveness compared to competing methods in various challenging scenarios.
Proposes a flexible MGP model for dynamic, sparse correlations.
problem Handling dynamic and sparse correlations in multivariate data.
method Non-stationary MGP with dynamic spike-and-slab prior and EM algorithm.
result Captures dynamic and sparse correlations effectively.
Extends variable screening for ultrahigh-dimensional models, reducing dimensionality to sample size.
problem Statistical inference challenges in ultrahigh-dimensional linear models.
method Extends correlation-based variable screening to arbitrary linear models and post-screening inference techniques.
result Shows a condition (screening condition) sufficient for successful variable screening in arbitrary linear models.
Unified Bayesian framework for efficient off-policy evaluation and learning in large action spaces.
problem Efficient off-policy evaluation and learning in systems with correlated actions.
method Unified Bayesian framework with structured priors and sDM approach.
result sDM leverages action correlations without compromising computational efficiency.
Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and gr…
New method learns frequency-dependent partial correlations.
problem Learning dependencies across distinct frequency bands.
method Formulate and solve two nonconvex learning problems.
result Proposed methods outperform existing state of the art.
DICCA maps multi-view data into a shared latent space with interpretable components.
problem Learning from multiple related but distinct data views.
method DICCA extends CCA to deep generative networks and uses sparsity-inducing priors for interpretability.
result DICCA effectively disentangles shared and view-specific variations in multi-view data.
This paper improves image super-resolution by integrating cross-scale non-local attention.
problem Improving image super-resolution by leveraging long-range and cross-scale feature correlations.
method Proposes a Cross-Scale Non-Local (CS-NL) attention module integrated into a recurrent neural network.
result Significantly improved performance on SISR benchmarks.
We integrate camera pose correlations into deep models using Gaussian processes.
problem Lack of inter-frame reasoning in deep neural networks.
method Derive a principled framework combining camera pose information with deep models using a novel view kernel.
result Soft-prior knowledge aids pose-related vision tasks like novel view synthesis.
New research challenges the flatness-generalization link in deep neural networks.
problem The correlation between flatness of the loss landscape and generalization in deep neural networks is questioned.
method The study examines various flatness measures and popular SGD variants, finding some break the flatness-generalization link. It proposes using logP(f), a global quantity, as a predictor of generalization. result The log of Bayesian prior upon initialization, logP(f), is a significantly more robust predictor of generalization than flatness measures. 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.
Transformer-based method for causal discovery with prior knowledge integration.
problem Complex nonlinear dependencies and spurious correlations in time series data.
method Multi-layer Transformer forecaster with gradient-based causal structure extraction and attention masking for prior knowledge integration.
result Significant improvement in causal discovery and causal lag estimation compared to state-of-the-art methods.
MAXENT method outperforms ML in sparse data with specific prior correlations.
problem Evaluating MAXENT method's validity limits and comparing it with ML.
method Bayesian decision theory, Dirichlet density, KL distance, regularized maximum likelihood.
result MAXENT can outperform ML in sparse data with specific prior correlations.
The paper introduces a new method for modeling correlations in high-dimensional data.
problem Modeling correlations among multiple dimensions in high-dimensional data.
method Population random measure embedding (PRME) using random functions and neural networks.
result The proposed method can efficiently learn correlations among multiple dimensions.
This paper compares log-likelihood and BLEU scores for sequence generation tasks.
problem The discrepancy between density estimation and sequence generation performance.
method Comparing several density estimators on five machine translation tasks.
result The correlation between log-likelihood and BLEU varies depending on model families.
Paper forecasts corporate default risk using Particle MCMC with expert opinions.
problem Predicting corporate default risk in the U.S. market.
method Bayesian approach with Particle Markov Chain Monte Carlo (Particle MCMC) algorithm.
result Volatility and mean reversion of hidden factor significantly impact default intensities.
VAE improves MCMC efficiency by generating diverse prior proposals.
problem Inefficient MCMC methods in Bayesian inverse problems, especially subsurface flow modeling.
method Uses Variational Autoencoder (VAE) to generate broader-spectrum prior proposals.
result VAE achieves comparable accuracy to Karhunen-Loève Expansion (KLE) and outperforms it when correlation length is unknown.
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
problem Infinite CNNs lose spatial correlations, which are crucial for their performance.
method Introduced correlated weights to restore spatial correlations in infinite CNNs.
result Optimal performance is achieved with a moderate level of weight correlation.
Proposes a method to enhance multi-view learning by maximizing higher order correlations.
problem Losing intrinsic interconnections among multiple views in pairwise correlation maximization.
method Formulates multi-view data as a low rank approximation problem using higher order correlation tensor and solves it with the generating polynomial method.
result Consistently outperforms prior methods on real multi-view data.
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
problem Rare spurious correlations in neural networks and their privacy risks.
method Introducing spurious patterns correlated with a fixed class to a few training examples, analyzing ℓ2 regularization and Gaussian noise. result Rare spurious correlations can significantly impact neural network accuracy and privacy, and specific mitigation methods can be effective.
We introduce a new test for detection of power-law cross-correlations among a pair of time series - the rescaled covariance test. The test is based on a power-law divergence of the covariance of the partial sums of the long-range cross-correlated processes. Utilizing a heteroskedasticity and auto-correlation robust est…
We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive timesteps, based on the data. We derive approximate variational inference procedur…
Paper predicts demographics at finer geographic resolutions using geotagged tweets.
problem Limited traditional survey methods for demographics estimates at finer geographic resolutions.
method Adapting prior work to predict gender and race/ethnicity counts at the blockgroup-level.
result Achieves high correlations (0.671 for gender, 0.692 for race) compared to prior work.
New study shows FTRL mechanism works with correlated events.
problem Forecasting competitions with correlated events.
method Introduces block correlation and uses FTRL mechanism.
result FTRL mechanism retains ε-optimal guarantee with O(b2log(n)/ε2) events for correlated events. Proposes deep weight prior for improving neural network performance.
problem Improving neural network performance with limited training data.
method Defines deep weight prior (DWP) as an implicit distribution and proposes variational inference methods.
result Improves performance of Bayesian neural networks with limited data and accelerates conventional CNN training.
A new method learns label correlations for better multi-label predictions.
problem Label correlations not accurately characterized by existing approaches.
method Sparse reconstruction in the label space to learn correlations, then integrate into model training.
result Our approach outperforms state-of-the-art multi-label learning methods.
Framework uses DL and STORM priors for FBU cardiac MRI reconstruction.
problem Reconstructing FBU cardiac MRI from undersampled data.
method Model-based reconstruction with DL and STORM priors.
result Demonstrates potential for accelerating FBU cardiac MRI.
PMI-Masking improves MLM pretraining by masking correlated spans efficiently.
problem Uniform token masking leads to inefficient and suboptimal performance in MLMs.
method PMI-Masking uses Pointwise Mutual Information to mask n-grams with high collocation.
result PMI-Masking reaches half the training time and improves performance.
Improves joint distribution learning for high-dimensional datasets with complex correlations.
problem Conditional independence assumption limitations in VAE decoders for high-dimensional datasets.
method Cramer-Wold distance regularization and two-step learning method for flexible prior modeling.
result Effective joint distributional learning for high-dimensional datasets with multiple categorical variables.
A new test statistic counts tree co-occurrences to detect edge correlation between networks.
problem Detecting edge correlation between networks using latent vertex correspondence.
method The test statistic is based on counting co-occurrences of signed trees for a family of non-isomorphic trees.
result The test runs in n2+o(1) time and succeeds with high probability for large n. Bayesian neural networks with functional priors improve surrogate modeling in mechanics.
problem Challenges in integrating prior knowledge and quantifying uncertainties in high-dimensional NN parameter spaces.
method Anchored ensembling to integrate a priori information and learn low-rank correlations between NN parameters.
result Effective transfer of knowledge between function-space and parameter-space priors improves surrogate model accuracy and uncertainty estimation.
Improved sample complexity for Gaussian Mixture Models using Pair Correlation Factor.
problem Understanding the sample complexity of Gaussian Mixture Models.
method Introducing Pair Correlation Factor (PCF) to measure clustering of component means and improving sample complexity bounds.
result The Pair Correlation Factor (PCF) more accurately determines the difficulty of parameter recovery in Gaussian Mixture Models.
D2PCCA integrates deep learning and probabilistic modeling for nonlinear dynamical systems.
problem Analyzing nonlinear dynamical systems with probabilistic understanding.
method Combines deep learning and probabilistic modeling, using KL annealing and normalizing flows.
result Captures latent dynamics in sequential datasets with improved convergence and flexibility.
Proposes a Bayesian Autoencoder with sparse Gaussian process priors to capture data correlations.
problem Autoencoders' i.i.d. assumption of latent representations fails to capture data correlations.
method Imposes fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder and uses stochastic gradient Hamiltonian Monte Carlo for posterior estimation.
result Consistently outperforms alternatives relying on Variational Autoencoders on various tasks.
Bayesian framework captures correlations in discrete environments for better decision-making.
problem Capturing correlations in discrete state-action domains for better decision-making.
method Bayesian learning framework based on Pólya-Gamma augmentation.
result Superior predictive performance compared to correlation-agnostic models.
Emergent misalignment is influenced by training dynamics, model priors, and data.
problem Emergent misalignment in models
method Exploring training dynamics, model priors, and data
result Activation deltas before and after narrow fine-tuning correlate with their similarities when measured with the last prompt-token activations.
GRASP simplifies Bayesian regression with grouped predictors using an adaptive NBP prior.
problem Regression with grouped predictors and adaptive shrinkage.
method Normal Beta Prime (NBP) prior with tunable hyperparameters for flexible sparsity control.
result Empirical validation of robust and versatile GRASP across various sparsity and signal-to-noise ratios.