The advent of high-throughput sequencing technologies made it possible to obtain large
volumes of genetic information, quickly and inexpensively. Thus, many efforts are devoted to …

Historically, Recurrent neural networks (RNNs) and its variants such as LSTM and GRU and
more recently Transformers have been the standard go-to components when processing …

Up until recently Recurrent neural networks (RNNs) have been the standard go-to component
when processing sequential data with neural networks. Issues relative to vanishing …

Motivation The advent of high-throughput sequencing technologies made it possible to obtain
large volumes of genetic information, quickly and inexpensively. Thus, many efforts are …

This volume contains 17 extended abstracts and 23 short abstracts representing a total of
40 proceedings papers presented at the 26th International Conference on Research in …

Neural Controlled Differential Equations (Neural CDEs) are the continuous-time analogue of
an RNN, just as Neural ODEs are analogous to ResNets. However just like RNNs, training …

Recently, Transformers have been widely used in various fields and have achieved remarkable
results. But it is still difficult for Transformer-based models to process longer sequences …

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across
its layers. Empirical evidence shows that UTs have better compositional generalization …

… dans les lettres soviétiques, Alexandre Sourkov, faisant le point de … Pour Sourkov aussi, les
années soixante marquent un … par un dogmatique impénitent, Vsevolod Kotchetov, a paru, en …

Recent advent of the second and third generation of sequencing has uncovered many
novel transcripts. These novel transcripts could have crucial functions in different biological …