Simulation-Driven Approach for Business Rules Discovery

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TiC-SAT: Tightly-coupled Systolic Accelerator for Transformers

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Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constraine ...

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Framing the News: From Human Perception to Large Language Model Inferences

Daniel Gatica-Perez

Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, ...

Assoc Computing Machinery2023

Dense Image-based Predictions for Comics Analysis

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Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

EPFL2023

Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Karl Aberer, Rémi Philippe Lebret, Mohammadreza Banaei

Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks. However, their enormous size often makes them impractical on memory-constrained devices, requiring practitioners to compress them to smaller net ...

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Modeling Structured Data in Attention-based Models

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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 ...

EPFL2023

XTab: Cross-table Pretraining for Tabular Transformers

Mahsa Shoaran, Bingzhao Zhu

The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple data t ...

JMLR.org2023

Regularization Techniques for Low-Resource Machine Translation

Alejandro Ramírez Atrio

Neural machine translation (MT) and text generation have recently reached very high levels of quality. However, both areas share a problem: in order to reach these levels, they require massive amounts of data. When this is not present, they lack generaliza ...

EPFL2023

Simulating supercontinua from mixed and cascaded nonlinearities

Luis Guillermo Villanueva Torrijo, Victor Brasch, Furkan Ayhan, Tobias Herr, Thibault Voumard

Nonlinear optical frequency conversion is of fundamental importance in photonics and underpins countless of its applications: Sum- and difference-frequency generation in media with quadratic nonlinearity permits reaching otherwise inaccessible wavelength r ...

AIP Publishing2023

Opportunities, Challenges, and Pitfalls in Making, Characterizing, and Understanding Polymer Brushes

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Polymer brushes have been theoretically described, their behavior under diverse conditions has been modeled, and their preparation via surface-initiated polymerizations (SIPs) has been progressively refined. However, despite three decades of research endea ...

American Chemical Society2023

Harnessing Rule-Based Chatbots to Support Teaching Python Programming Best Practices

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Adrian Christian Holzer

In recent years, the use of chatbots in education has been driven by advances in natural language processing and the increasing availability of digital education platforms. Although the added value of educational chatbots appears promising, researchers hav ...

Springer2023

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