Publications related to Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Related publications (16)

Deep Architectural Form

With recent innovations in artificial intelligence (AI), machines can now recognize architectural patterns and generate new forms in ways that far exceed human capacity. The integration of machines in the design process has ushered in a substantial shift i ...

EPFL2024

Learning Motion Reconstruction from Demonstration via Multi-Modal Soft Tactile Sensing

Josephine Anna Eleanor Hughes, Kai Christian Junge, Kieran Daniel Gilday, Cheng Pan

Learning manipulation from demonstration is a key way for humans to teach complex tasks. However, this domain mainly focuses on kinetic teaching, and does not consider imitation of interaction forces which is essential for more contact rich tasks. We propo ...

Institute of Electrical and Electronics Engineers Inc.2024

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Andreas Thomas Marfurt

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, ...

EPFL2023

Perspective Aware Road Obstacle Detection

Pascal Fua, Mathieu Salzmann, Sina Honari, Krzysztof Maciej Lis

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by c ...

2023

A benchmark dataset for machine learning in ecotoxicology

Kristin Schirmer, Fernando Perez Cruz

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, ...

Nature Portfolio2023

Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

Georg Michael Joachim Starke

The increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the fi ...

2023

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

Corinne Louise Jones

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

2022

Measurement in the Age of Information

Marilyne Andersen, Forrest Simon Webler

Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from ...

2022

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Emmanuel Pignat, Teguh Santoso Lembono, Julius Maximilian Jankowski

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

The role of population receptive field size and recurrent processing in learning to “de-crowd”

Michael Herzog, Ayberk Ozkirli, Maya Anna Jastrzebowska

In crowding, the perception of a target is impeded by surrounding clutter. While traditional models are feedforward and local, there is increasing behavioral and neural evidence for a critical role of recurrent processing across the visual hierarchy in cro ...

SAGE Publishing2021

MPGM: Scalable and Accurate Multiple Network Alignment

Matthias Grossglauser, Ehsan Kazemi

Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...

IEEE COMPUTER SOC2020

Temporal analysis of multimodal data to predict collaborative learning outcomes

Kshitij Sharma, Jennifer Kaitlyn Olsen

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated ...

WILEY2020

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Blagovesta Hristova Kostova

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously ...

ASSOC COMPUTING MACHINERY2020

The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism

Chang-Hyun Park

Introduction For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have b ...

2020

A Geometry-Inspired Decision-Based Attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli

Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by que ...

IEEE COMPUTER SOC2019