Search Results for author:
Found 14 papers, 5 papers with code
mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning
This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022.
The IICT-Yverdon System for the WMT 2021 Unsupervised MT and Very Low Resource Supervised MT Task
We then present the application of this system to the 2021 task for low-resource supervised Upper Sorbian (HSB) to German translation, in both directions.
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
To evaluate the robustness of NMT models to our attack, we propose enhancements to existing black-box word-replacement-based attacks by incorporating output translations of the target NMT model and the output logits of a classifier within the attack process.
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models
First, we propose an optimization problem to generate adversarial examples that are semantically similar to the original sentences but destroy the translation generated by the target NMT model.
Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT
Subword tokenization is the de facto standard for tokenization in neural language models and machine translation systems.
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning
In this paper, we show that a class of evolutionary algorithms (EAs) inspired by the Gillespie-Orr Mutational Landscapes model for natural evolution is formally equivalent to SGD in certain settings and, in practice, is well adapted to large ANNs.
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
We then leverage this definition to show that a general class of gradient-free ML algorithms - ($1,\lambda$)-Evolutionary Search - can be combined with classical distributed consensus algorithms to generate gradient-free byzantine-resilient distributed learning algorithms.
Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models).
Targeted Adversarial Attacks against Neural Machine Translation
In this paper, we propose a new targeted adversarial attack against NMT models.
TransFool: An Adversarial Attack against Neural Machine Translation Models
Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks.
Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale
The advent of the internet, followed shortly by the social media made it ubiquitous in consuming and sharing information between anyone with access to it.
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers
Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples.
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach
This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies.
Evolutionary perspective on model fine-tuning
Be it in natural language generation or in the image generation, massive performances gains have been achieved in the last years.
