A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
MMVAE learns multi-modal data with shared and private latent spaces.
COBRA reduces modality gap in cross-modal tasks.
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact descriptio…
In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders …
The paper analyzes and proposes an algorithm for multi-modal nonlinear embeddings with theoretical performance bounds.
The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often esta…
Proposes a novel method for detecting novelty in multi-modal data.
MoCA uses a novel autoencoder to analyze multi-modal health data.
Efficient video captioning model captures cross-modal interactions.
Paper extends RPD for better handling multiple modalities and non-convexity.
Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear…
MMGAN stabilizes GANs for multi-modal data clustering.
The paper proves probabilistic alignment between unseen modalities using contrastive learning.
Modality-agnostic compression improves across diverse data types.
Unified model learns joint and individual features from brain imaging data.
T-EMDE bridges the heterogeneity gap between image and text modalities.
GROOVE learns representations for weakly paired multimodal data.
AF improves sampling from high-dimensional, multi-modal distributions.
Enhances CLIP's similarity computation using PMI's linear structure.
Organizations adapt ML models to new data types using existing resources.
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. Visual and semantic feature spaces have different structures by definition. For certain concepts, vis…
Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs)…
New method identifies shared components from unpaired multimodal mixtures.
This paper tackles multi-modal label disentanglement in partition-based XMC.
DiGS improves sampling from multi-modal distributions.
KD-Net transfers knowledge from multi-modal to mono-modal segmentation networks.
Feed-forward networks are widely used in cross-modal applications to bridge modalities by mapping distributed vectors of one modality to the other, or to a shared space. The predicted vectors are then used to perform e.g., retrieval or labeling. Thus, the success of the whole system relies on the ability of the mapping…
HyperLearn learns unified representations from multiple modalities efficiently.
MHVAE learns cross-modality inference inspired by human cognition.
Increasingly many real world tasks involve data in multiple modalities or views. This has motivated the development of many effective algorithms for learning a common latent space to relate multiple domains. However, most existing cross-view learning algorithms assume access to paired data for training. Their applicabi…
GACEM optimizes complex multi-modal problems using neural networks.
The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much mor…
Proposes a novel network for CTR prediction by learning modality-specific and modality-invariant representations.
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to fully leverage different modalities due to practical challenges such as varying levels of noise and conflicts between modalities. Existing metho…
We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that…
In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set of different types of sensors. Exploiting complementary information from different sensors, we show that target detection and classificatio…
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…
This research shows how to learn shared representations from unpaired data.
Improves video search by balancing text and visual modalities.
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work l…
We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for…
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…
Framework for handling long-tailed multi-modal data.
TAP transfers knowledge from unlabeled data to improve cross-modal learning.
SiMLR reduces complex biomedical data into simpler, interpretable forms.
We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference tasks. This is achieved by obtaining an -way tensor to consider the high-order relationships between modalities and the output laye…
DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints bet…