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The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a ...

Video hashing is a technique of encoding videos into binary vectors, facilitating efficient video storage and high-speed computation. Current approaches to video hashing predominantly utilize sequential frame images to produce semantic binary codes. ...

Smart glasses, augmented by advances in multimodal Large Language Models (LLMs), are at the forefront of creating ubiquitous Artificial General Intelligence (AGI). This short literature survey reviews the latest developments in integrating LLMs with ...

This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-...

In today’s interconnected world, achieving reliable out-of-distribution (OOD) detection poses a significant challenge for machine learning models. While numerous studies have introduced improved approaches for multi-class OOD detection tasks, the ...

The use of Large Language Models (LLMs) is revolutionizing the legal industry. In this technical talk, we would like to explore the various use cases of LLMs in legal tasks, discuss the best practices, investigate the available resources, examine the ...

The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate a variety of tasks in the legal domain, such as legal search and research, legal document review and ...

The ability to detect out-of-distribution (OOD) inputs is essential for safely deploying machine learning models in an open world. Most existing research on OOD detection, and more generally uncertainty quantification, has focused on multi-class ...

This full-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and ...

For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types ...

This half-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and ...

Hashing, which represents data items as compact binary codes, has been becoming a more and more popular technique, e.g., for large-scale image retrieval, owing to its super fast search speed as well as its extremely economical memory consumption. However,...

There is strong interest in leveraging reinforcement learning (RL) for information retrieval (IR) applications including search, recommendation, and advertising. Just in 2020, the term "reinforcement learning" was mentioned in more than 60 different ...

Personalized news recommendation can help users stay on top of the current affairs without being overwhelmed by the endless torrents of online news. However, the freshness or timeliness of news has been largely ignored by current news recommendation ...

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty‐three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but ...

Learning to hash is a fundamental technique widely used in large-scale image retrieval. Most existing methods for learning to hash address the involved discrete optimization problem by the continuous relaxation of the binary constraint, which usually ...

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics ...

In this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelled as a two-player strategic game: one player, a query formulator, taking ...

It is of great interest to researchers and scholars in many disciplines (particularly those working on cultural heritage projects) to study parallel passages (i.e., identical or similar pieces of text describing the same thing) in digital text archives. ...

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given ...