AF improves sampling from high-dimensional, multi-modal distributions.
problem Sampling from high-dimensional, multi-modal distributions is challenging.
method Annealing Flow (AF) using Continuous Normalizing Flow (CNF) with dynamic Optimal Transport (OT) objective and annealing procedures.
result AF significantly improves training efficiency and stability, outperforming state-of-the-art methods.
A new MCMC method improves HMC for distant multi-modal sampling.
problem Sampling from multi-modal distributions, especially when modes are far apart.
method Improves Hamiltonian dynamics and uses variational distribution.
result The method can converge to target distributions effectively.
The paper evaluates samplers on multi-modal targets, focusing on mode separation and recovery.
problem Handling multi-modality in sampling.
method Synthetic experimental setting focusing on mode relative importance recovery.
result Illustrates the challenges and potential of samplers in multi-modality.
New method samples from multi-modal distributions on Riemannian manifolds without training.
problem Sampling from multi-modal distributions on Riemannian manifolds is challenging.
method Simulation of a non-equilibrium deterministic dynamics to transport noise toward target distributions.
result Method is entirely training-free and effective on various multi-modal problems.
DiGS improves sampling from multi-modal distributions.
problem Inadequate mixing in MCMC methods for multi-modal distributions.
method Integrates diffusion models and Gibbs sampling to create an auxiliary noisy distribution.
result DiGS exhibits better mixing for multi-modal distributions than state-of-the-art methods.
Unified Bayesian model for multi-modal, small sample size biomedical data classification.
problem Classifying high-dimensional, multi-modal biomedical data with small sample sizes.
method Combines multi-modal data views into a latent space, prunes irrelevant features, and uses dual kernels for small sample size scenarios.
result Outperforms state-of-the-art models and identifies features aligned with existing markers.
New method trains neural samplers to sample from multi-modal distributions efficiently.
problem Mode-seeking behavior of reverse KL divergence hinders effective sampling from multi-modal target distributions.
method Minimizing reverse diffusive KL divergence along diffusion trajectories of model and target densities.
result Demonstrated enhanced sampling performance across various multi-modal distributions.
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal …
The paper analyzes and proposes an algorithm for multi-modal nonlinear embeddings with theoretical performance bounds.
problem Generalizability of multi-modal nonlinear embeddings to unseen data.
method Theoretical analysis and a multi-modal nonlinear representation learning algorithm motivated by performance bounds.
result The proposed algorithm yields promising performance in multi-modal image classification and cross-modal image-text retrieval applications.
ROME improves density estimation for multi-modal, non-normal data.
problem Robust multi-modal density estimation in non-normal, highly correlated distributions.
method ROME uses clustering to segment multi-modal data into uni-modal clusters, then combines KDE estimates for each cluster.
result ROME outperforms state-of-the-art methods and is more robust to various distributions.
A new method samples from multi-modal distributions without hyperparameter tuning.
problem Sampling from multi-modal distributions is challenging and requires tuning hyperparameters.
method Learned Reference-based Diffusion Sampler (LRDS) that learns a reference model on high-density regions and uses it to train a diffusion-based sampler.
result LRDS best exploits prior knowledge on multi-modal distributions compared to competing algorithms.
A novel multi-modal active learning approach using RL for engagement estimation.
problem Challenges in labeling multi-modal human data for accurate user state estimation.
method Deep reinforcement learning for optimal data selection and multi-modal data fusion.
result The proposed approach outperforms existing methods in engagement estimation.
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC…
The paper improves SMC algorithm for multi-modal distributions by proving variance bounds.
problem Problems with SMC on multi-modal distributions, especially in terms of mixing time.
method Proves variance bounds for SMC on multi-modal distributions using soft decomposition.
result Bounds on SMC variance depend on local rather than global mixing times.
Diffusion models learn multi-modal distributions with optimal efficiency.
problem Learning high-dimensional distributions with low-dimensional multi-modal structures.
method Score-based diffusion models, focusing on subgaussian distributions within subspaces.
result Diffusion models require O ~ ( ε − k ∨ 2 ) \widetilde{O}(\varepsilon^{-k \vee 2}) O ( ε − k ∨ 2 ) samples for 1-Wasserstein ε \varepsilon ε error, improving over prior guarantees. SIG combines explicit and implicit models for better sample diversity.
problem Mode collapsing in GANs and multi-modality samples.
method Introduces semi-implicit generator (SIG) as a hierarchical model trained in maximum likelihood.
result SIG improves GANs' ability to generate diverse samples, stabilizing training and resisting mode collapse.
Improved PDMP samplers for multi-modal distributions using tempering.
problem Struggles of PDMP samplers with multi-modal or heavy-tailed distributions.
method Tempering PDMPs by interpolating between a tractable and posterior distribution.
result PDMP samplers can be improved to sample from multi-modal distributions.
Paper develops a theory explaining contrastive pre-training for multimodal AI.
problem Limited theoretical understanding of contrastive pre-training for multi-modal AI.
method Introduces approximate sufficient statistics and Joint Generative Hierarchical Model.
result Near-minimizers of contrastive loss are approximately sufficient, enabling diverse downstream tasks.
A new algorithm flattens multi-modal distributions for better deep learning.
problem Bayesian learning in big data with multi-modal distributions.
method Contour Stochastic Gradient Langevin Dynamics (CSGLD) algorithm.
result The CSGLD algorithm avoids local traps in deep neural networks.
A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few works have provided "beyond worst-case" guarantees for such settings. For log-c…
This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a certain sample through metric learning. We start by using…
GACEM optimizes complex multi-modal problems using neural networks.
problem Black-box optimization and constraint satisfaction in multi-modal environments.
method Modified Cross-Entropy Method with masked auto-regressive neural network.
result GACEM outperforms traditional CEM in diverse solutions, mode discovery, and sample efficiency.
Piecewise normalizing flows improve multi-modal distribution modeling.
problem Improving accuracy in modeling multi-modal distributions.
method Divide target distribution into clusters, train flows to match standard normal base.
result Piecewise flows outperform standard approaches in accuracy.
MultiPath predicts multi-modal future trajectories for better motion planning.
problem Predicting human behavior in uncertain real-world domains like autonomous driving.
method Leverages fixed future state-sequence anchors and regresses offsets with uncertainties.
result Achieves more accurate predictions with an order of magnitude fewer trajectories.
Study reveals limits of PLS in multi-modal learning with correlated signals.
problem Understanding PLS performance in multi-modal learning with correlated signals.
method Random matrix theory analysis of spiked cross-covariance models.
result Identifies SNR and correlation regimes where PLS fails to recover any signal.
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
problem Robustness to both innovative and additive outliers in Kalman filtering.
method Particle mixture Kalman filter with re-sampling of past states.
result CE-BASS efficiently handles multi-modality and trend changes in hidden state distributions.
New algorithm speeds up sampling from complex Bayesian mixture models.
problem Sampling from non-log-concave, multi-modal posterior distributions in Bayesian Gaussian mixtures.
method Introduced Reflected Metropolis-Hastings Random Walk (RMRW) algorithm.
result Proved mixing time bound for RMRW in symmetric two-component Gaussian mixtures.
FlowVAT improves variational inference for multi-modal distributions.
problem Mode-seeking behavior and collapse in variational inference for complex posteriors.
method Conditional tempering approach for normalizing flow variational inference.
result FlowVAT outperforms traditional and adaptive annealing methods in multi-modal distributions, finding more modes and achieving better ELBO values.
A new Monte Carlo sampling method derived from reverse diffusion.
problem Sampling from complex distributions, especially multi-modal ones.
method Transforming score matching into mean estimation; estimating means of regularized posterior distributions.
result rdMC can approximate sampling with any desired accuracy and is significantly faster than MCMC for complex distributions.
New method for sampling from complex distributions using stochastic localization.
problem Sampling from unnormalized target densities in multi-modal distributions.
method Stochastic Localization via Iterative Posterior Sampling (SLIPS) framework.
result Approximate samples from target distribution and denoiser learned iteratively.
MoCA uses a novel autoencoder to analyze multi-modal health data.
problem Challenges in analyzing continuous multi-modal health data from wearable devices.
method Proposes MoCA, a self-supervised learning framework combining transformer and masked autoencoder methods.
result Demonstrates strong performance boosts across reconstruction and classification tasks.
EIM algorithm maximizes information projection for multi-modal data modeling.
problem Challenging task of modeling highly multi-modal data.
method Expected Information Maximization (EIM) algorithm using variational upper bound.
result EIM algorithm efficiently optimizes the I-projection for Gaussian mixtures models.
Framework for reconstructing nonlinear systems from multi-modal time series data.
problem Reconstructing nonlinear dynamical systems from multi-modal time series data.
method Dynamic interpretable recurrent neural networks coupled with generalized linear models for multi-modal data integration.
result Framework efficiently compensates for noisy or missing information in one data channel using other channels.
New algorithms select and rank features from MTS without feature extraction.
problem Feature extraction step for MTS classification.
method Directly computes similarity between time series and assesses cluster structure matching labels.
result Techniques match labels well without feature extraction.
Geometric tempering fails for Langevin dynamics, proving convergence limits.
problem Proving convergence and limitations of geometric tempering for Langevin dynamics.
method Theoretical investigation of geometric tempering using Langevin dynamics.
result Geometric tempering can lead to exponential time convergence and poor functional inequalities.
Develops multi-modal neural network models for improved prediction and uncertainty quantification.
problem Improving prediction accuracy and uncertainty quantification for multi-modal data.
method Multi-modal Bayesian neural network models with conjugate last-layer estimation using SVI.
result Improved prediction accuracy and uncertainty quantification compared to uni-modal models.
METEOR learns efficient representations from multi-modal data streams.
problem Efficiently interpreting multi-modal information in complex environments.
method METEOR learns compact representations by sharing parameters within semantically meaningful groups and preserving domain-agnostic semantics.
result METEOR reduces memory usage by around 80% compared to conventional methods.
Framework for handling long-tailed multi-modal data.
problem Class imbalance and long-tailed distributions in multi-modal data.
method Multi-expert architecture with modality-specific networks and dynamic fusion weights.
result Framework outperforms existing methods in long-tailed, class-imbalanced scenarios.
New framework shows cross-attention improves multi-modal in-context learning.
problem Understanding multi-modal in-context learning in neural networks.
method Mathematical framework and linearized cross-attention mechanism.
result Cross-attention mechanism is provably optimal for multi-modal in-context learning.
Improves likelihood-free inference by using a new sampling approach to avoid biased data collection.
problem Efficient Bayesian inference without likelihood evaluation for real-world datasets.
method Introduces Neural Proposal (NP) to sample simulation inputs i.i.d. for unbiased posterior inference.
result Demonstrates improved performance, especially for multi-modal posteriors, through experiments.
This paper improves sentiment classification by combining text, audio, and video data using DCCA.
problem Improving sentiment classification accuracy using multi-modal data.
method Deep Canonical Correlation Analysis (DCCA) for combining text, audio, and video embeddings.
result One-Step DCCA outperforms current state-of-the-art in multi-modal embedding learning.
Contrastive learning adapts to data intrinsic dimensions, learning low-dimensional representations.
problem Learning high-dimensional representations from multi-modal data.
method Multi-modal contrastive learning with temperature optimization.
result Contrastive learning adapts to intrinsic dimensions of data, not specified dimensions.
Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
problem Fixed anchor methods limit multi-modal learning by over-reliance on a single modality and inadequate cross-modal correlation.
method Adaptive anchor methods using centroid-based anchors from all modalities.
result Adaptive anchor methods like CentroBind consistently outperform fixed anchor methods across various datasets.
PyTorch Frame simplifies multi-modal tabular learning with modular data and model handling.
problem Handling complex multi-modal tabular data in deep learning.
method A PyTorch-based framework that provides a data structure, model abstraction, and integration with external models.
result Demonstrated the effectiveness of PyTorch Frame in implementing and applying diverse tabular models to complex multi-modal tabular data.
Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a classifier on image features in the common representations and apply it to the testing of the text features in the representations. Existing m…
MEx dataset benchmarks HAR and multi-modal fusion for exercise quality.
problem Recognizing and evaluating exercise quality for Musculoskeletal Disorders patients.
method Multi-sensor, multi-modal dataset with four sensors (pressure mat, depth camera, accelerometers) for HAR and exercise quality assessment.
result Reference performance for each sensor identified, exposing their strengths and weaknesses.
Paper proposes multi-task learning for multi-modal video Q&A.
problem Expensive to create large-scale datasets for multi-modal video Q&A.
method Composed of three networks: video Q&A, temporal retrieval, and modality alignment.
result State-of-the-art results on TVQA dataset.
MMVAE learns multi-modal data with shared and private latent spaces.
problem Learning useful representations across multiple data modalities.
method Mixture-of-experts variational autoencoder (MMVAE).
result MMVAE satisfies four criteria for multi-modal learning.