arXiv research

MuLan links music audio to natural language tags.

Multimodal ML models can process data in multiple modalities (e.g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e.g., object detection, scene understanding). In this paper, we focus on the problem of video categorization by using a multimodal approach. We have develop…

The paper proves probabilistic alignment between unseen modalities using contrastive learning.

Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compare…

We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully conne…

In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by fusing the knowledge from the various modalities independently for performing emotion classification. In contrast to these appr…

Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at q…

Shifts dataset evaluates uncertainty in real-world tasks across modalities.

Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive v…

Proposes QQE for transforming and embedding data distributions.

Maps can be embedded in higher dimensions if they lift to embeddings in product spaces.

New embeddings for manifolds using heat kernels.

Introduces PELP for graph-enhanced word embeddings.

Curvature regularization prevents distortion in graph embeddings.

Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of w…

Proposes cone embedding for better graph hierarchical structure representation.

Classifies linear embeddings of grassmannians and ind-grassmannians.

BC-Aligner maintains backward compatibility of embeddings after frequent updates.

Embedding calculus proves convergence for surfaces.

Unified framework for word embedding models using noise examples.

Models use embeddings and attention for better claim severity prediction.

A fast graph embedding method for large graphs.

Paper proves impossibility of three desirable properties in node embedding.

The study characterizes and verifies equivariant embeddings of symmetric Kählerian manifolds.

Proves uniqueness of embedding complex manifold into infinite-dimensional space.

The paper defines invariants for almost graph embeddings and explores their properties.

Recently, click-through rate (CTR) prediction models have evolved from shallow methods to deep neural networks. Most deep CTR models follow an Embedding\&MLP paradigm, that is, first mapping discrete id features, e.g. user visited items, into low dimensional vectors with an embedding module, then learn a multi-layer pe…

This work analyzes PPR-based node embeddings and their topological information.

Network representation learning in low dimensional vector space has attracted considerable attention in both academic and industrial domains. Most real-world networks are dynamic with addition/deletion of nodes and edges. The existing graph embedding methods are designed for static networks and they cannot capture evol…

For leveled spatial graphs, we find a surface embedding that allows cellular embedding.

Long spacelike embeddings can be approximated by isometric ones.

A {\it wrinkled embedding} $f:V^n\to W^m$ is a topological embedding which is a smooth embedding everywhere on $V$ except a set of $(n-1)$-dimensional spheres, where $f$ has cuspidal corners. In this paper we prove that any rotation of the tangent plane field $TV\subset TW$ of a {\it smoothly embedded} submanifold $V\s…

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, whi…

Graphs embeddable on torus and linklessly in 3D can be embedded linklessly in standard torus.

Geometrically transforms word embeddings into a common space for better comparison.

Obtaining continuous representations of structural data such as directed acyclic graphs (DAGs) has gained attention in machine learning and artificial intelligence. However, embedding complex DAGs in which both ancestors and descendants of nodes are exponentially increasing is difficult. Tackling in this problem, we de…

The paper proves nonexistence and existence results for minimal surfaces in R^4.

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it i…

MCE reduces embedding instability in nonlinear dimensionality reduction.

Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec. Recently, the same idea has been applied on source code with encouraging results. In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code. The a…

Paper uses JIVE to decompose word embeddings, improving sentiment analysis performance.

Well-quasi-orders proved on embedded planar graphs.

We seek to better understand the difference in quality of the several publicly released embeddings. We propose several tasks that help to distinguish the characteristics of different embeddings. Our evaluation of sentiment polarity and synonym/antonym relations shows that embeddings are able to capture surprisingly nua…

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. We therefore propose a framework for parallel computat…

We create interpretable word embeddings through sparse coding.

The purpose of this article is to investigate the relationship between suborbifolds and orbifold embeddings. In particular, we give natural definitions of the notion of suborbifold and orbifold embedding and provide many examples. Surprisingly, we show that there are (topologically embedded) smooth suborbifolds which d…

Paper studies embedding conditions for homogeneous quandles.

Sparse OSEs achieve optimal embedding dimension of O(d).