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Stéphane Ayache

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3 papers

2 author rows

AAAI Conference 2022 Conference Paper

Are Vision-Language Transformers Learning Multimodal Representations? A Probing Perspective

Emmanuelle Salin
Badreddine Farah
Stéphane Ayache
Benoit Favre

In recent years, joint text-image embeddings have significantly improved thanks to the development of transformerbased Vision-Language models. Despite these advances, we still need to better understand the representations produced by those models. In this paper, we compare pre-trained and finetuned representations at a vision, language and multimodal level. To that end, we use a set of probing tasks to evaluate the performance of state-of-the-art Vision-Language models and introduce new datasets specifically for multimodal probing. These datasets are carefully designed to address a range of multimodal capabilities while minimizing the potential for models to rely on bias. Although the results confirm the ability of Vision-Language models to understand color at a multimodal level, the models seem to prefer relying on bias in text data for object position and size. On semantically adversarial examples, we find that those models are able to pinpoint finegrained multimodal differences. Finally, we also notice that fine-tuning a Vision-Language model on multimodal tasks does not necessarily improve its multimodal ability. We make all datasets and code available to replicate experiments.

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ICML Conference 2022 Conference Paper

Implicit Regularization with Polynomial Growth in Deep Tensor Factorization

Kais Hariz
Hachem Kadri
Stéphane Ayache
Maher Moakher
Thierry Artières

We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and ’shallow’ tensor factorization via linear and certain type of non-linear neural networks promotes low-rank solutions with at most quadratic growth, we show that its effect in deep tensor factorization grows polynomially with the depth of the network. This provides a remarkably faithful description of the observed experimental behaviour. Using numerical experiments, we demonstrate the benefits of this implicit regularization in yielding a more accurate estimation and better convergence properties.

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ICML Conference 2020 Conference Paper

Partial Trace Regression and Low-Rank Kraus Decomposition

Hachem Kadri
Stéphane Ayache
Riikka Huusari
Alain Rakotomamonjy
Liva Ralaivola

The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more general model, namely the partial-trace regression model, a family of linear mappings from matrix-valued inputs to matrix-valued outputs; this model subsumes the trace regression model and thus the linear regression model. Borrowing tools from quantum information theory, where partial trace operators have been extensively studied, we propose a framework for learning partial trace regression models from data by taking advantage of the so-called low-rank Kraus representation of completely positive maps. We show the relevance of our framework with synthetic and real-world experiments conducted for both i) matrix-to-matrix regression and ii) positive semidefinite matrix completion, two tasks which can be formulated as partial trace regression problems.

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