Publications related to Estimating and Improving the Robustness of Attributions in Text

Rethinking data augmentation for adversarial robustness

Recent work has proposed novel data augmentation methods to improve the adversarial robustness of deep neural networks. In this paper, we re-evaluate such methods through the lens of different metrics that characterize the augmented manifold, finding contr ...

2023

Exploring neural networks and their potential for species distribution modeling

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Lloyd Haydn Hughes

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Berlin2023

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Pascal Frossard, Ádám Dániel Ivánkay

Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight into t ...

Assoc Computational Linguistics-Acl2023

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

Linear Complexity Self-Attention With 3rd Order Polynomials

Grigorios Chrysos, Filippos Kokkinos

Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with the nu ...

Los Alamitos2023

4M: Massively Multimodal Masked Modeling

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar, Roman Christian Bachmann, David Mizrahi

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Neural Information Processing Systems (Nips)2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

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

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

IEEE2023

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

Devis Tuia, Marc Conrad Russwurm, Corinna Barbara Frank

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Estimating and Improving the Robustness of Attributions in Text

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Rethinking data augmentation for adversarial robustness

Exploring neural networks and their potential for species distribution modeling

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Linear Complexity Self-Attention With 3rd Order Polynomials

4M: Massively Multimodal Masked Modeling

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Exploring neural networks and their potential for species distribution modeling

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Rethinking data augmentation for adversarial robustness

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Linear Complexity Self-Attention With 3rd Order Polynomials

Interpretable Representation Learning and Evaluation for Abstractive Summarization

4M: Massively Multimodal Masked Modeling

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks