Publications associées à An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

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Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

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Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Investigating cognitive ability using action-based models of structural brain networks

The structural connectome and motor recovery after stroke: predicting natural recovery

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Learning Bollobas-Riordan Graphs Under Partial Observability

Local Tomography of Large Networks Under the Low-Observability Regime

Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks

Simplicity in complex networks: a path-based centrality

A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system

The structural connectome and motor recovery after stroke: predicting natural recovery

A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system

Simplicity in complex networks: a path-based centrality

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

Learning Bollobas-Riordan Graphs Under Partial Observability

Local Tomography of Large Networks Under the Low-Observability Regime

Investigating cognitive ability using action-based models of structural brain networks

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks