WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

We study the drainage of a viscous liquid film coating the outside of a solid horizontal cylinder, where gravity acts vertically. We focus on the limit of large Ohnesorge numbers Oh, where inertia is negligible compared to viscous effects. We first study t ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flex ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

When a plasma disrupts in a tokamak, significant heat and electromagnetic loads are deposited onto the surrounding device components. These forces scale with plasma current and magnetic field strength, making disruptions one of the key challenges for futur ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Animals exhibit remarkable locomotion skills despite significant sensorimotor delays and operating in uncertain environments. Moreover, mammals acquire these skills within minutes of birth. From the Cambrian explosion to the present day, vertebrate motor c ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

What changes with age? Results in reinforcement learning (RL) are mixed. Some studies found deteriorated RL performance in older participants compared to younger controls whereas other studies did not. Daniel et al. (J Neurosci 40(5):1084–1096, 2020. 10.15 ...

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern informa ...

Web search engines process billions of queries daily, making the balance between computational efficiency and ranking quality crucial. While neural ranking models have shown impressive performance, their computational costs, particularly in feature extract ...

Accurate and reproducible color characterization is essential for colored building integrated photovoltaic products, both for manufacturing quality control and assessing long-term color stability. However, existing characterization techniques struggle to a ...

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider ...

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

We introduce BLAP, a model capable of generating high-quality captions for music. BLAP leverages a fine-tuned CLAP audio encoder and a pre-trained Flan-T5 large language model. To achieve effective cross-modal alignment between music and language, BLAP uti ...

Megapixel single-photon avalanche diode (SPAD) arrays have been developed recently, opening up the possibility of deploying SPADs as general-purpose passive cameras for photography and computer vision. However, most previous work on SPADs has been limited ...

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

Chat with Graph Search

Stability of a liquid layer draining around a horizontal cylinder: Interplay of capillary and gravity forces

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

CHAMELEON: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Predicting the long-term collective behaviour of fish pairs with deep learning

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Plasma state monitoring and disruption characterization using multimodal VAEs

Graph generative deep learning models with an application to circuit topologies

A Bio-Inspired Learning and Control Framework for Cross-Embodiment and Cross-Task Locomotion

Efficient local linearity regularization to overcome catastrophic overfitting

Intact reinforcement learning in healthy ageing

The 2024 Magnonics Roadmap

Cost-Efficiency Trade-offs for Neural Cascade Rankers in Web Search

Accurate color characterization of solar photovoltaic modules for building integration

Neural Distributed Image Compression with Cross-Attention Feature Alignment

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Bootstrapping Language-Audio Pre-training for Music Captioning

Seeing Photons in Color