1 citations · 2 across the 6 of their papers we have counts for
12 papers
Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations
Chiara Semenzin, Faadil Mustun, Roberto Dessi +5
Self-supervised learning (SSL) has opened new opportunities in bioacoustics by enabling scalable modeling of animal vocalizations without the need for expensive manual annotation.…
What Makes Two Language Models Think Alike?
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar represe…
Polar probe linearly decodes semantic structures from LLMs
Pablo J. Diego-Simón, Pierre Orhan, Emmanuel Chemla +2
How do artificial neural networks bind concepts to form complex semantic structures? Here, we propose a simple neural code, whereby the existence and the type of relations between…
MapFormer: Self-Supervised Learning of Cognitive Maps with Input-Dependent Positional Embeddings
Victor Rambaud, Salvador Mascarenhas, Yair Lakretz
A cognitive map is an internal model which encodes the abstract relationships among entities in the world, giving humans and animals the flexibility to adapt to new situations, wit…
Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks
Pierre Orhan, Pablo Diego-Simón, Emmnanuel Chemla +3
During language acquisition, children successively learn to categorize phonemes, identify words, and combine them with syntax to form new meaning. While the development of this beh…
Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding…