Our paper accepted at ACM BCB 2014


I just got notified that our paper entitled “Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions” has been accepted for ACM BCB 2014, the 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics.

This article was written by me, Peter J. Sadowski and Pierre Baldi from University of California Irvine, and reports my project developed during my six month stay in the Orange County.

ACM BCB 2014 conference will be held in Newport Beach (Southern California, USA) in late September 2014. See you there!

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My favourite papers and talks at Nips 2013

I just came back from NIPS 2013, an illustrious world conference on machine learning in South Lake Tahoe, California.

Here’s the best papers and talks from the conference:

  • “Dropout training as adaptive regularization” by Stefan Wager, Sida Wang, Percy Liang (Stanford). Interesting research in which authors attribute the dropout algorithm training to an adaptive regularizer, and find common aspects with AdaGrad, an online learning method based on the adaptive gradient descent.
  • “Adaptive dropout for training deep neural networks” by Jimmy Ba, Brendan Frey (University of Toronto). Again on the dropout algorithm, authors investigate some alternatives to picking 0.5 as unity dropout probability, during the dropout training.
  • “Understanding dropout” by Pierre Baldi, Peter J. Sadowski (University of California Irvine). Authors investigate important mathematical issues of the dropout algorithm.
  • “Training and Analysing Deep Recurrent Neural Networks”¬† by Michiel Hermans, Benjamin Schrauwen (Universiteit Gent). Authors apply deep recurrent neural networks to sequence time series prediction, and show some interesting applications to the character sequence prediction of English Wikipedia text.
  • “Deep supervised and convolutional generative stochastic network for protein secondary structure prediction” by Jian Zhou and Olga Troyanskaya (Princeton), from the Deep Learning workshop. An interesting application of generative stochastic network (GSN) to the important issue of the protein secondary structure prediction.
  • “Tissue-dependent alternative splicing prediction using deep neural network” by Michaek K. K. Leung, Hui Yuan Xiong, Leo J. Lee and Brendan J. Frey (University of Toronto), from the Machine Learning in Computational Biology workshop. Authors apply the dropout algorithm in a deep neural network to predict new regions of tissue alternative splicing.

A really top-level conference with intriguing workshops… thanks a lot to all the organizers!

[EDIT: check out these blog posts by Paul Mineiro, hundalhh, Yisong Yue, Sebastien Bubeck, Memming]

Blog da visitare: SiliconValley.Corriere.it


Dopo avervi segnalato un po’ di siti web e blog relativi alla citta’ di Milano, che dovreste visitare, oggi voglio parlarvi d’un interessantissimo blog che tratta i temi della tecnologia, della ricerca scientifica, dell’universita’ e della fuga di cervelli: sto parlando di SiliconValley.Corriere.it, di Marco Marinucci per il Corriere della Sera online.

Per stimolare la vostra curiosita’, ecco cosa si puo’ leggere alla sezione “chi siamo”

Siamo manager, professionisti, professori universitari, imprenditori la cui attivit√† e’ basata in Silicon Valley, chi da 2, chi da 10, chi da 35 anni.
Ci accomuna un senso di positività di base, un interesse per le tecnologie e un certo impeto a voler contribuire a smuovere la nostra Italia dal torpore attuale.

SiliconValley.Corriere.it