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Investigating Cross-lingual Multi-level Adaptive Networks: The Importance of the Correlation of Source and Target Languages

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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Leveraging Unlabeled Data to Track Memorization

Patrick Thiran, Mahsa Forouzesh, Hanie Sedghi

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, ca ...

2023

Neural Basis of Internal Attention in Adults with Pure and Comorbid ADHD

Farnaz Delavari, Halima Rafi

Objective: To examine whether putatively atypical neuronal activity during internal attention in ADHD yields insights into processes underlying emotion dysregulation. Methods: We used a word processing paradigm to assess neural activations in adults with A ...

2023

El paisaje como imagen afectiva

Lucia-Nieves Garcia de Jalon Oyarzun

On his way to a political philosophy founded not on how we would like things to be, but on how they are, the philosopher Spinoza had to take a step back and develop an ethics that presented affects as properties of bodies. From there, and in order to deter ...

Abada2023

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Angelos Katharopoulos

Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...

EPFL2022

TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Nevena Dresevic

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

2022

Neural-network quantum states for periodic systems in continuous space

Giuseppe Carleo, Gabriel Maria Pescia

We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parametrized in terms of a permutationally invariant part described by the Deep Sets neural-n ...

AMER PHYSICAL SOC2022

3D Common Corruptions and Data Augmentation

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar, Andrei Atanov

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike e ...

IEEE COMPUTER SOC2022

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Pierre Dillenbourg, Arzu Güneysu Özgür, Nevena Dresevic, Nihal Ezgi Yücetürk, Marija Katanic

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

2022

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This paper describes a system for interactive poem generation, which combines neural language models (LMs) for poem generation with explicit constraints that can be set by users on form, topic, emotion, and rhyming scheme. LMs cannot learn such constraints ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2022