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While some methods are confined to linear embeddings and others exhibit limited robustness, high-dimensional time series factorization techniques employ scalable matrix factorization for forecasting in latent space. This paper introduces a novel ...

Many engineering applications rely on simulations based on partial differential equations. Different numerical schemes to approximate solutions exist. These schemes typically require setting parameters to appropriately model the problem at hand. ...

We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials science. Our model systematically combines VAE with regression, ...

In this paper, we propose a Weighted Cross-modal Aggregation network (WCAN) for rumor detection in order to combine highly correlated features in different modalities and obtain a unified representation in the same space. WCAN exploits an ...

Social media echo chambers are known to be common sources of misinformation and harmful ideologies that have detrimental impacts on society. Therefore, techniques to detect echo chambers are of great significance. Reinforcement of supporting ...

Despite the success of prompt learning-based models in text generation tasks, they still suffer from the introduction of external commonsense knowledge, especially from biased knowledge introduction. In this work, we propose KiProL, a knowledge-...

The WSDM 2023 Toloka VQA challenge introduces a new Grounding-based Visual Question Answering (GVQA) dataset, elevating multimodal task complexity. This challenge diverges from traditional VQA by requiring models to identify a bounding box in ...

The detection of misinformation as “fake news” is vital for a well-informed and highly functioning society. Most of the recent works on the identification of fake news make use of deep learning and large language models to achieve high levels of ...

Recent Query Auto-completion (QAC) systems leverage natural language generation or pre-trained language models (PLMs) to demonstrate remarkable performance. However, these systems also suffer from biased and toxic completions. Efforts have been ...${}^{\mathrm{}}$${}^{\mathrm{}}$

This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications. While language models like BERT have improved prediction accuracy, their token input constraints limit the ...

In the realm of fake news detection, conventional Graph Neural Network (GNN) methods are often hamstrung by their dependency on non-textual auxiliary data for graph construction, such as user interactions and content spread patterns, which are not ...

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem that entails the extraction of multifaceted aspects, opinions, and sentiments from the given text. Both standalone and compound ABSA tasks have been extensively used in ...

One of the primary challenges in online learning environments is to retain learner engagement. Several different instructional strategies are proposed both in online and offline environments to enhance learner engagement. The Concept Attainment ...

Given one or more time sequences, how can we extract their governing equations? Single and co-evolving time sequences appear in numerous settings, including medicine (neuroscience - EEG signals, cardiology - EKG), epidemiology (covid/flu spreading ...

Current work on Active Learning (AL) tends to assume that the relationship between input and target variables does not change, i.e., the oracle is static. However, oracles can be stream-like and exhibit concept drift, which requires updating the ...

Graph Recurrent Neural Networks (GRNN) excel in time-series prediction by modeling complicated non-linear relationships among time-series. However, most GRNN models target small datasets that only have tens of time-series or hundreds of time-...$\mathrm{}$$\mathrm{}$$\mathrm{}$$\mathrm{}$

Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing noises that can fool a DNN model into believing that there is nothing (...

Multivariate time series representation learning employs unsupervised tasks to extract meaningful representations from time series data, enabling their application in diverse downstream tasks. However, despite the promising advancements in ...

Time series data plays a significant role in many research fields since it can record and disclose the dynamic trends of a phenomenon with a sequence of ordered data points. Time series data is dynamic, of variable length, and often contains ...

Correlations between variables in complex ecosystems such as weather and financial markets lead to a great amount of dynamic and co-evolving time series data, posing a significant challenge to the current forecast methods. Discovering dynamic ...

Time series forecasting attempts to predict future events by analyzing past trends and patterns. Although well researched, certain critical aspects pertaining to the use of deep learning in time series forecasting remain ambiguous. Our research ...

Semi-supervised time series classification has become an increasingly popular task due to the limited availability of labeled data in practice. Recently, Similarity-aware Time Series Classification (SimTSC) has been proposed to address the label ...

Reliable forecasting of traffic flow requires efficient modeling of traffic data. Indeed, different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different ...