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.
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.
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.
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.
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.
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.
This work improves multi-modal generative models by using permutation-invariant neural networks.
problem Improving multi-modal generative models with tighter variational objectives.
method Developed more flexible aggregation schemes based on permutation-invariant neural networks.
result Our variational objective and flexible aggregation models can better approximate the true joint distribution.
This paper reviews deep learning for multi-modality medical image segmentation.
problem Improving segmentation accuracy in medical images using multiple modalities.
method Overview of deep learning and multi-modal medical image segmentation, analysis of different network architectures and fusion strategies.
result Later fusion of modalities can lead to more accurate segmentation results.
Method predicts NBA players' multi-modal movement trajectories.
problem Understanding NBA players' decision-making during games.
method LSTM-based architecture with multi-modal loss function.
result Method outperforms state-of-the-art in predicting realistic trajectories.
This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework also allows investigation of the relative contributions of the individual views in the final multi-modal embedding. Individual features deriv…
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 …
This study optimizes multi-modal learning thresholds and algorithms in high dimensions.
problem Optimizing multi-modal learning performance in high-dimensional data.
method Analytical quantification and derivation of AMP algorithm with state evolution analysis.
result Bayes-optimal performance and recovery thresholds derived for multi-modal data.
ESE-FN improves elderly activity recognition accuracy.
problem Recognizing individual actions and human-object interactions in elderly activities.
method Exploits multi-modal features from RGB videos and skeleton sequences using ESE attentions and a new Multi-modal Loss.
result ESE-FN achieves best accuracy on ETRI-Activity3D dataset.
Paper introduces topological eigenvalue theorems for tensor analysis in multi-modal data.
problem Lack of deep understanding of tensor structures in multi-modal data fusion.
method Introduces topological perspective to tensor eigenvalue analysis, linking eigenvalues to topological features.
result Establishes new theorems that enhance understanding of tensor structures in data fusion.
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and human-robot interactions, multi-modal models of the users states (e.g., engagement) are therefore needed. Most of the existing works that try t…
GPCCA integrates multi-modal data with missing values, improving clustering accuracy.
problem Integrating and analyzing multi-modal data with missing values and partial observations.
method Generalized Probabilistic Canonical Correlation Analysis (GPCCA) for unsupervised multi-modal data integration and dimensionality reduction.
result GPCCA outperforms existing methods in capturing essential patterns across modalities and provides robust low-dimensional embeddings.
Proposes a novel method for detecting novelty in multi-modal data.
problem Challenges in detecting novelty in high-dimensional, multi-modal data.
method Orthogonalized latent space for disentangling features and defining novelty score.
result Proposed method outperforms state-of-the-art algorithms in novelty detection.
New divergences improve score-based methods for multi-modal distributions.
problem Blindness problem in score-based divergences for multi-modal distributions.
method Proposed a new family of divergences to mitigate blindness.
result Improved performance in density estimation compared to traditional approaches.
Study uses sentiment analysis to predict cryptocurrency token returns in virtual reality.
problem Predicting cryptocurrency token returns in virtual reality economies.
method Used BERT for sentiment analysis and developed LSTM models integrating multi-modal features.
result Multi-modal model significantly outperforms price-only baseline in prediction accuracy.
This work tackles uncertainty in multi-agent multi-modal trajectory forecasting.
problem Measuring and ranking uncertainty in multi-agent multi-modal trajectory forecasting.
method Proposes collaborative uncertainty (CU) and a CU-aware regression framework.
result The CU-aware regression framework improves SOTA systems' performances.
New method for multi-modal depth prediction challenges.
problem Challenges in applying unimodal coreset selection to multi-modal data.
method Adapted state-of-the-art coreset selection technique for multimodal data.
result Challenges in extending unimodal algorithms to multi-modal scenarios.
This paper rates robustness of multi-modal time-series forecasting models.
problem Robustness of AI systems in time-series forecasting is crucial for stakeholders.
method Causal analysis to assess robustness of MM-TSFM models.
result Multi-modal forecasting models are more robust than numeric models.
This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (M 2 ^2 2 VAE).
This paper tackles multi-modal label disentanglement in partition-based XMC.
problem Existing partition-based XMC methods create mutually exclusive clusters, which is sub-optimal for multi-modal labels.
method Formulates label assignment as an optimization problem to maximize precision rates, creating flexible and overlapped label clusters.
result Successfully disentangles multi-modal labels, leading to state-of-the-art results on XMC benchmarks.
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.
Study reveals efficient recovery of multi-modal signals via Bayesian methods and sequential learning.
problem Recovering multiple high-dimensional signals from correlated modalities.
method Bayesian Approximate Message Passing and Sequential Curriculum Learning.
result Sequential learning strategy optimally recovers weak signals in multi-modal settings.
MEx: Multi-modal Exercises Dataset is a multi-sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskelet…
With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels ar…
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering. Multi-modal video question answering is an important task that aims at the joint understanding of vision and language. However, establishing large scale dataset for multi-modal video question answeri…
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.
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.
Proposes a multi-modal attention network for better stock price prediction.
problem Predicting future stock movements using historical records and social media.
method Extracts semantic information from social media, estimates credibility, and integrates with numeric features.
result Significantly improved prediction accuracy and trading profits compared to previous methods.
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.
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.
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.
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. Paper presents ECL dataset for multi-modal bankruptcy prediction.
problem Developing models to predict corporate bankruptcy using textual and numerical data.
method Used ECL dataset to develop and evaluate classical and neural models.
result Textual and numerical data modalities complement each other for bankruptcy prediction.
A new memory-based fusion layer improves multi-modal deep learning performance.
problem Improving performance of multi-modal deep learning by addressing long-term dependencies.
method Introducing a Memory based Attentive Fusion (MBAF) layer that incorporates both current and long-term dependencies.
result The MBAF layer enhances fusion and improves performance across different modalities and networks.
Triple-GAIL learns from multiple sources to improve imitation learning for complex behaviors.
problem Limited scalability of GAIL in real-world scenarios like autonomous vehicles.
method Integrates expert demonstrations and generated experiences with an auxiliary skill selector.
result Triple-GAIL outperforms state-of-the-art methods in learning complex behaviors.
Stein-Encoder isolates genetic signals in multi-modal biomedical data.
problem Integration of high-dimensional genomic data with clinical data obscures genetic predictive impact.
method White-box supervised framework using Stein's method and residualization.
result Stein-Encoder improves predictive accuracy and reveals specific biological mechanisms.
A framework is proposed to detect anomalies in multi-modal data. A deep neural network-based object detector is employed to extract counts of objects and sub-events from the data. A cyclostationary model is proposed to model regular patterns of behavior in the count sequences. The anomaly detection problem is formulate…