1 citations · 2 across the 15 of their papers we have counts for
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Forward Only Learning for Orthogonal Neural Networks of any Depth
Paul Caillon, Alex Colagrande, Erwan Fagnou +2
Backpropagation is still the de facto algorithm used today to train neural networks. With the exponential growth of recent architectures, the computational cost of this algorithm a…
On the Stability of Neural Networks in Deep Learning
Blaise Delattre
Deep learning has achieved remarkable success across a wide range of tasks, but its models often suffer from instability and vulnerability: small changes to the input may drastical…
Bridging the Theoretical Gap in Randomized Smoothing
Blaise Delattre, Paul Caillon, Quentin Barthélemy +2
Randomized smoothing has become a leading approach for certifying adversarial robustness in machine learning models. However, a persistent gap remains between theoretical certified…
Conditional Distribution Quantization in Machine Learning
Blaise Delattre, Sylvain Delattre, Alexandre Vérine +1
Conditional expectation \mathbb{E}(Y \mid X) often fails to capture the complexity of multimodal conditional distributions \mathcal{L}(Y \mid X). To address this, we propose using…
Accelerated Training through Iterative Gradient Propagation Along the Residual Path
Erwan Fagnou, Paul Caillon, Blaise Delattre +1
Despite being the cornerstone of deep learning, backpropagation is criticized for its inherent sequentiality, which can limit the scalability of very deep models. Such models faced…