Browse Proceedings

Efficient time series forecasting has become critical for real-world applications, particularly with deep neural networks (DNNs). Efficiency in DNNs can be achieved through sparse connectivity and reducing the model size. However, finding the ...

Rumors have exerted detrimental effects on individuals and societies in recent years. Despite the deployment of sophisticated Graph Neural Networks (GNNs) to analyze the structure of propagation graphs in rumor detection, contemporary approaches ...

Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning. However, deep ensembles often come with high computational costs and memory ...

Recommender Systems (RSs) may inadvertently perpetuate biases based on protected attributes like gender, religion, or ethnicity. Left unaddressed, these biases can lead to unfair system behavior and privacy concerns. Interpretable RS models ...

Data augmentation is an important technique in training deep neural networks as it enhances their ability to generalize and remain robust. While data augmentation is commonly used to expand the sample size and act as a consistency regularization ...

Self-contrastive learning has proven effective for vision and natural language tasks. It aims to learn aligned data representations by encoding similar and dissimilar sentence pairs without human annotation. Therefore, data augmentation plays a ...

Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less frequent labels to balance the class distribution. Existing ...

We present MANTRA, a framework for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our method can compute probabilistically guaranteed high-quality temporal betweenness estimates (of nodes and temporal edges) ...$$$\stackrel{}{\mathcal{}}\frac{}{{}^{\mathrm{}}}\mathcal{}$$\mathcal{}$

Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences between in-...

Cross-domain sequential recommendation (CDSR) aims to address the data spCH). Recently, some SR approaches have utilized auxiliary behaviors to complement the information for long-tailed users. However, these methods cannot deliver promising ...

Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model. In the context of image ...

Learning at the edges has become increasingly important as large quantities of data are continuously generated locally. Among others, this paradigm requires algorithms that are simple (so that they can be executed by local devices), robust (...$\sqrt{}$

This work addresses the dual challenges of enhancing training efficiency and protecting data privacy in Vertical Federated Learning (VFL) through secure synthetic dataset generation. VFL typically involves an active party with labels collaborating ...

In a multiple instance learning (MIL) scenario, the outcome annotation is usually only reported at the bag level. Considering simplicity and convergence criteria, the lazy learning approach, i.e., k-nearest neighbors (kNN), plays a crucial role in ...

Entity Linking aims to map mentions in a document to corresponding entities in a given knowledge base. Most previous studies usually extract mentions and infer their underlying entities. An obvious limitation of this approach (mention-to-entities, ...

Session-based recommendation (SBR) aims to predict the next-interacted item based on an anonymous user behavior sequence (session). The main challenge is how to decipher the user intent with limited interactions. Recent progress regards the ...

Hierarchical fine-grained visual classification assigns multi-granularity labels to each object, forming a tree hierarchy. However, how to minimize the impact of coarse-grained classification errors on fine-grained classification and achieve high ...

Backdoor attack aims to induce neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems. Existing ...

We study label privacy protection in vertical federated learning (VFL). VFL enables an active party who possesses labeled data to improve model performance (utility) by collaborating with passive parties who have auxiliary features. Recently, ...$$$$

Developing task-oriented bots requires diverse sets of annotated user utterances to learn mappings between natural language utterances and user intents. Automated paraphrase generation offers a cost-effective and scalable approach for generating ...

In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing user data across domains, ...

As decision-making processes become increasingly complex, machine learning tools have become essential resources for tackling business and social issues. However, many methodologies rely on complex models that experts and everyday users cannot ...

Graph Neural Networks (GNNs) are susceptible to inheriting and even amplifying biases within datasets, subsequently leading to discriminatory decision-making. Our empirical observation reveals that the inconsistent distribution of sensitive ...

In this paper, we introduce a new framework that can be used for evaluating the validity and the performance of machine learning models on manifold-valued data. More particularly, two methods are detailed with theoretical properties for spherical ...

Large pre-trained language models (LPLM) have shown spectacular success when fine-tuned on downstream supervised tasks. It is known, however, that their performance can drastically drop when there is a distribution shift between the data used ...

A visual question answering (VQA) system for electrical circuit images could be useful as a quiz generator, design and verification assistant or an electrical diagnosis tool. Although there exists a vast literature on VQA, to the best of our ...$$