5 citations · 10 across the 6 of their papers we have counts for
8 papers
Evolution of Spectroscopic Factors in Neutron-deficient -shell Nuclei in connection with Short-Range Correlations
S. Koyama, D. Suzuki, O. Sorlin +31
We report an uncertainty-controlled determination of neutron-removal spectroscopic factors in the neutron-deficient -shell nuclei , , $^{9}\mat…
Unbound neutron strength in C and the N=16 shell gap
J. Lois-Fuentes, B. Fernández-Domínguez, F. Delaunay +61
Significant continuum strength has been observed to be populated in C produced in the d(C,p) reaction at a beam energy of 17.2~MeV/nucleon. The strength appears at gr…
Neutron-proton pairing in the unstable N=Z nuclei of the f-shell through two-nucleon transfer reactions
M. Assié, H. Jacob, Y. Blumenfeld +1
Pair transfer is a unique tool to study pairing correlations in nuclei. Neutron-proton pairing is investigated in the N=Z nuclei of the f-shell, through the reaction (p,3He) in inv…
N=16 magicity revealed at the proton drip-line through the study of 35Ca
L. Lalanne, O. Sorlin, A. Poves +27
The last proton bound calcium isotope Ca has been studied for the first time, using the Ca()Ca two neutron transfer reaction. The radioactive Ca n…
Cross-shell states in C: a test for p-sd interactions
J. Lois-Fuentes, B. Fernández-Domínguez, X. Pereira-López +59
The low-lying structure of C has been investigated via the neutron-removal C reaction. Along with bound neutron sd-shell hole states, unbound p-shell hole stat…
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
Deep neural networks are typically initialized with random weights, with variances chosen to facilitate signal propagation and stable gradients. It is also believed that diversity…
Is Feature Diversity Necessary in Neural Network Initialization?
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
Standard practice in training neural networks involves initializing the weights in an independent fashion. The results of recent work suggest that feature "diversity" at initializa…
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
Reducing the precision of weights and activation functions in neural network training, with minimal impact on performance, is essential for the deployment of these models in resour…