Our new paper “FADER: Fast Adversarial Example Detection” is out! Check it if you want to speed up adversarial examples detection! https://t.co/EUN3d60Dz9
@biggiobattista@MarcoMelisIT@sotgiu_angelo
Our paper "PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware" has been accepted to #DIMVA '19! See you in Sweden! (preprint here: https://t.co/KIDHKJYYVf - you can also try our tool: https://t.co/SmR6FxKq9T) #powershell#security#malware
At least I can say I had lunch with a Nobel prize (kind of 😃)! Two remains! Great congratulations to this ML three pioneers! @ylecun@geoffreyhinton https://t.co/MLn0cgNlYB
First journal paper accepted for publication on IEEE TNN-LS!
It applies DropIn technique to augment the resilience of recurrent neural networks to missing inputs.
Check this out at https://t.co/hUjYtchwuk!
#unipi#phd#research#deeplearning
I am thrilled to announce that our book “C++ Fundamentals” is finally ready! I’d like to express a sincere gratitude to my co-author @makersfz and to @PacktPub for helping us to achieve the goal.
AdvML & Game Theory. There's been lot of work beyond zero-sum (minimax) games - a much more elegant version of adversarial training - by Bruckner and Scheffer (JMLR, 2012).
We also worked on that w/ randomized classifiers/attackers (IEEE TNNLS, 2016): https://t.co/FdLUPVgBZk