Publications related to Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Jiancheng Yang, Li Zhang, Zhigang Li, Bo Du

Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesi ...

Cham2023

Interlocking modular microfluidic cooling substrates for future high energy physics experiments

Massimo Angeletti

In high energy physics, semiconductor-based sensors are widely used in particle tracking applications. These sensors are typically glued on low-mass cooling substrates, which guarantee the correct thermal management and, at the same time, minimise their in ...

EPFL2023

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the d ...

2023

Moments of the number of points in a bounded set for number field lattices

Maryna Viazovska, Vlad Serban, Nihar Prakash Gargava

We examine the moments of the number of lattice points in a fixed ball of volume

V

for lattices in Euclidean space which are modules over the ring of integers of a number field

K

. In particular, denoting by

ω_K

the number of roots of unity in

K

, we ...

arXiv2023

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Jiancheng Yang, Stanislav Lukyanenko

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

New York2023

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Jürg Alexander Schiffmann, Cyril Picard, Faez Ahmed

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significan ...

Amer Soc Mechanical Engineers2023

Monotonicity Reasoning in the Age of Neural Foundation Models

Zeming Chen, Qiyue Gao

The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our unde ...

Dordrecht2023

Chromatin modules and their implication in genomic organization and gene regulation

Bart Deplancke, Guido Van Mierlo, Judith Franziska Kribelbauer

Regulation of gene expression is a complex but highly guided process. While genomic technologies and computational approaches have allowed high-throughput mapping of cis-regulatory elements (CREs) and their interactions in 3D, their precise role in regulat ...

ELSEVIER SCIENCE LONDON2023

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Alexander Mathis, Mackenzie Mathis, Kai Jappe Sandbrink, Matthias Bethge, Pranav Mamidanna

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

eLIFE SCIENCES PUBL LTD2023

DISCO: Distilling Counterfactuals with Large Language Models

Antoine Bosselut, Zeming Chen, Qiyue Gao

Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsour ...

Assoc Computational Linguistics-Acl2023

Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift

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Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Interlocking modular microfluidic cooling substrates for future high energy physics experiments

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Moments of the number of points in a bounded set for number field lattices

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Monotonicity Reasoning in the Age of Neural Foundation Models

Chromatin modules and their implication in genomic organization and gene regulation

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

DISCO: Distilling Counterfactuals with Large Language Models

Interlocking modular microfluidic cooling substrates for future high energy physics experiments

Moments of the number of points in a bounded set for number field lattices

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

DISCO: Distilling Counterfactuals with Large Language Models

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Chromatin modules and their implication in genomic organization and gene regulation

Monotonicity Reasoning in the Age of Neural Foundation Models