Lightweight Cross-Lingual Sentence Representation Learning

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Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data's inherent structure or context by solving a pretext task. With ...

This article introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt eng ...

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

This chapter explores the intersection of generative AI and personalized learning, emphasizing the role of educators and examining the application of large language models (LLMs) in crafting personalized learning paths. The chapter examines the perception ...

We are motivated primarily by the adaptation of text-to-speech synthesis models; however we argue that more generic parameter-efficient fine-tuning (PEFT) is an appropriate framework to do such adaptation. Nevertheless, catastrophic forgetting remains an i ...

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scal ...

Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these system ...

In this paper, we use synthetic data and propose a new method for template inversion attacks against face recognition systems. We use synthetic data to train a face reconstruction model to generate high-resolution (i.e., 1024 x 1024) face images from facia ...

In the information age, the Web and the growing global connectivity drastically simplified our access to information. Learning and fact-checking from online resources is nowadays part of our daily routine. Studying the dynamic associated with online conten ...

Test-time domain adaptation aims to adapt a source pretrained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in n ...

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge graph, or from ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of handling dialogs which also involve unstructured knowledge (e.g. reviews and doc ...

This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

Lightweight Cross-Lingual Sentence Representation Learning

Chat with Graph Search

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification

Generative AI Literacy: Twelve Defining Competencies

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Beter Support Ideation Than Text Prompts for Difusion Models

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Can Generative AI Support Educators? Creating Learning Paths with PolyGloT

Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting

How to build the virtual cell with artificial intelligence: Priorities and opportunities

Course Recommender Systems Need to Consider the Job Market

Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition Systems

How We Use Wikipedia: Studying Readers' Behavior with Navigation Traces

Continual Test-Time Domain Adaptation

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

SKILL: Structured Knowledge Infusion for Large Language Models

Modeling Structured Data in Attention-based Models

4M: Massively Multimodal Masked Modeling

Interpretable Representation Learning and Evaluation for Abstractive Summarization

End-to-End Task-Oriented Dialog Modeling With Semi-Structured Knowledge Management

Text as a Richer Source of Supervision in Semantic Segmentation Tasks