Next meeting:
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Total votes:
4
(last vote was 2 years ago)
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Total votes:
2
(last vote was 1 year ago)
Title:
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Total votes:
2
(last vote was 1 year ago)
Author:
Discussion leader:
Nobody volunteered yet
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Total votes:
1
(last vote was 3 months ago)
Author:
J. François, L. Ravera
Discussion leader:
Nobody volunteered yet
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Total votes:
1
(last vote was 5 months ago)
Author:
Gerardo Aldazabal, Eduardo Andrés, Anamaría Font, Kumar Narain, Ida G. Zadeh
Discussion leader:
Nobody volunteered yet
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Total votes:
1
(last vote was 1 year ago)
Author:
Claudio Andrea Manzari, Yujin Park, Benjamin R. Safdi, Inbar Savoray
Discussion leader:
Nobody volunteered yet
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Total votes:
1
(last vote was 1 year ago)
Title:
Author:
David Alesini, Danilo Babusci, Paolo Beltrame, Fabio Bossi, Paolo Ciambrone, Alessandro D'Elia, Daniele Di Gioacchino, Giampiero Di Pirro et al.
Discussion leader:
Nobody volunteered yet
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Total votes:
1
(last vote was 1 year ago)
Title:
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Total votes:
1
(last vote was 1 year ago)
Author:
Florian Goertz, Álvaro Pastor-Gutiérrez
Discussion leader:
Nobody volunteered yet
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6 years ago
Title: GRRMHD Simulations of Tidal Disruption Event Accretion Disks around Supermassive Black Holes: Jet Fo
Link: https://arxiv.org/abs/1811.06971
Description:
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6 years ago
Title: Machine Learning Binarized Neural Networks: Training Deep Neural Networks with We
Link: https://arxiv.org/pdf/1602.02830.pdf
Description: We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.
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6 years ago
Title: Distinguishing Boson Stars from Black Holes and Neutron Stars from Tidal Interactions in Inspiraling
Link: https://arxiv.org/abs/1704.08651
Description:
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6 years ago
Title: Highly-boosted dark matter and cutoff for cosmic-ray neutrino through neutrino portal
Link: https://arxiv.org/pdf/1809.08610.pdf
Description:
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6 years ago
Title: Phonon creation by gravitational waves
Link: https://arxiv.org/abs/1402.7009
Description:
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