9 citations · 12 across the 9 of their papers we have counts for
3 papers · 1 filter
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
Kacper Kapuśniak, Manuel Burger, Gunnar Rätsch +1
The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, empha…
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Alexandru Meterez, Amir Joudaki, Francesco Orabona +3
Normalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, b…
On the impact of activation and normalization in obtaining isometric embeddings at initialization
Amir Joudaki, Hadi Daneshmand, Francis Bach
In this paper, we explore the structure of the penultimate Gram matrix in deep neural networks, which contains the pairwise inner products of outputs corresponding to a batch of in…