GenIE: Generative Information Extraction

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Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhanc ...

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting point for downstream tasks. While these models are undeniably useful, it is a c ...

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL ...

The current information landscape is characterised by a vast amount of relatively semantically homogeneous, when observed in isolation, data silos that are, however, drastically semantically fragmented when considered as a whole. Within each data silo, inf ...

The current manufacturing trend is towards sustainability. Due to the degradation of natural resources and the accumulation of waste with an increase in the world population and in the consumption of goods, many countries attempt to switch from a linear to ...

Formalised knowledge systems, including universities and research institutes, are important for contemporary societies. They are, however, also arguably failing humanity when their impact is measured against the level of progress being made in stimulating ...

Investigating the intangible nature of a cultural domain can take multiple forms, addressing for example the aesthetic, epistemic and social dimensions of its phenomenology. The context of Southern Chinese martial arts is of particular significance as it c ...

Machine-readable semantic knowledge in the form of taxonomies (i.e., a collection of is-a edges) has proved to be beneficial in an array of NLP tasks including inference, textual entailment, question answering and information extraction. Such widespread ut ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Transformer-based models have revolutionized NLP. But in general, these models are highly resource consuming. Based on this consideration, several reservoir computing approaches to NLP have shown promising results. In this context, we propose EsnTorch, a l ...

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

Neuromorphic systems provide biologically inspired methods of computing, alternative to the classical von Neumann approach. In these systems, computation is performed by a network of spiking neurons controlled by the values of their synaptic weights, which ...

GenIE: Generative Information Extraction

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Mechanical intelligence for learning embodied sensor-object relationships

Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations

Optimal performance of Graph Convolutional Networks on the Contextual Stochastic Block Model

Interpreting Language Models Through Knowledge Graph Extraction

Representation Learning for Multi-relational Data

Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Ground-based image deconvolution with Swin Transformer UNet

Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations

Approaching Ontology Alignment through Representation Learning to Bridge the Semantic Gap in Engineering Applications

A semantic-driven approach toward Industry 4.0

Transforming knowledge systems for life on Earth: Visions of future systems and how to get there

The Facets of Intangible Heritage in Southern Chinese Martial Arts: Applying a Knowledge-Driven Cultural Contact Detection Approach

Automated Taxonomy Induction and its Applications

Learning sparse features can lead to overfitting in neural networks

The Esntorch Library: Efficient Implementation of Transformer-based Echo State Networks

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Neuromorphic System with Phase-Change Synapses for Pattern Learning and Feature Extraction