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Found 24 papers, 10 papers with code
Transport Optimal pour le Changement Sémantique à partir de Plongements Contextualisés (Optimal Transport for Semantic Change Detection using Contextualised Embeddings )
Plusieurs méthodes de détection des changements sémantiques utilisant des plongements lexicaux contextualisés sont apparues récemment.
Chain and Causal Attention for Efficient Entity Tracking
This paper investigates the limitations of transformers for entity-tracking tasks in large language models.
Exploring Precision and Recall to assess the quality and diversity of LLMs
We introduce a novel evaluation framework for Large Language Models (LLMs) such as \textsc{Llama-2} and \textsc{Mistral}, focusing on importing Precision and Recall metrics from image generation to text generation.
LOCOST: State-Space Models for Long Document Abstractive Summarization
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies.
Spectral Norm of Convolutional Layers with Circular and Zero Paddings
This paper leverages the use of \emph{Gram iteration} an efficient, deterministic, and differentiable method for computing spectral norm with an upper bound guarantee.
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
In this paper, we introduce a novel differentially private generative modeling approach based on a gradient flow in the space of probability measures.
The Lipschitz-Variance-Margin Tradeoff for Enhanced Randomized Smoothing
The certified radius in this context is a crucial indicator of the robustness of models.
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing.
Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge.
A Unified Algebraic Perspective on Lipschitz Neural Networks
Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.
Ranked #1 on
Provable Adversarial Defense
on CIFAR-100
Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling
Neural Ordinary Differential Equations (ODEs) was recently introduced as a new family of neural network models, which relies on black-box ODE solvers for inference and training.
Curriculum learning for data-driven modeling of dynamical systems
Based on that, we consider entropy as a metric of complexity of the dataset; we show how an informed design of the training set based on the analysis of the entropy significantly improves the resulting models in terms of generalizability, and provide insights on the amount and the choice of data required for an effective data-driven modeling.
A Dynamical System Perspective for Lipschitz Neural Networks
The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples.
Measure and Evaluation of Semantic Divergence across Two Languages
We propose a set of scenarios to characterize semantic divergence across two languages, along with a setup to differentiate them in a bilingual corpus.
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In this paper, we propose LeBenchmark: a reproducible framework for assessing SSL from speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
FlauBERT: Unsupervised Language Model Pre-training for French
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Ranked #2 on
Natural Language Inference
on XNLI French
Empirical Study of Diachronic Word Embeddings for Scarce Data
Word meaning change can be inferred from drifts of time-varying word embeddings.
Learning dynamic word embeddings with drift regularisation
Word usage, meaning and connotation change throughout time.
Exploring sentence informativeness
This study is a preliminary exploration of the concept of informativeness -how much information a sentence gives about a word it contains- and its potential benefits to building quality word representations from scarce data.
Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes
Usage similarity estimation addresses the semantic proximity of word instances in different contexts.
Towards Recognizing Phrase Translation Processes: Experiments on English-French
This work demonstrates that it is possible to automatically classify translation processes.
Document Neural Autoregressive Distribution Estimation
We present an approach based on feed-forward neural networks for learning the distribution of textual documents.
