Travelled to:1 × Australia
1 × United Kingdom
2 × China
3 × USA
Collaborated with:M.Denil D.Matheson Y.D.Mizrahi Z.Wang S.Mohamed A.J.Smola M.Zoghi D.Kotzias P.Smyth L.Bazzani H.Larochelle V.Murino J.Ting K.Swersky M.Ranzato D.Buchman B.M.Marlin M.Klaas M.Briers A.Doucet S.Maskell D.Lang
Talks about:random (3) particl (2) forest (2) learn (2) deep (2) hamiltonian (1) determinist (1) exponenti (1) autoencod (1) recognit (1)
Person: Nando de Freitas
DBLP: Freitas:Nando_de
Contributed to:
2015
201420142013201220112006Wrote 9 papers:
- KDD-2015-KotziasDFS #using
- From Group to Individual Labels Using Deep Features (DK, MD, NdF, PS), pp. 597–606.
- ICML-c1-2014-DenilMF #random
- Narrowing the Gap: Random Forests In Theory and In Practice (MD, DM, NdF), pp. 665–673.
- ICML-c2-2014-MizrahiDF #learning #linear #markov #parallel #random
- Linear and Parallel Learning of Markov Random Fields (YDM, MD, NdF), pp. 199–207.
- ICML-c3-2013-DenilMF #consistency #online #random
- Consistency of Online Random Forests (MD, DM, NdF), pp. 1256–1264.
- ICML-c3-2013-WangMF #adaptation #monte carlo
- Adaptive Hamiltonian and Riemann Manifold Monte Carlo (ZW, SM, NdF), pp. 1462–1470.
- ICML-2012-FreitasSZ #bound #exponential #process
- Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (NdF, AJS, MZ), p. 125.
- ICML-2011-BazzaniFLMT #learning #network #policy #recognition #video
- Learning attentional policies for tracking and recognition in video with deep networks (LB, NdF, HL, VM, JAT), pp. 937–944.
- ICML-2011-SwerskyRBMF #energy #modelling #on the
- On Autoencoders and Score Matching for Energy Based Models (KS, MR, DB, BMM, NdF), pp. 1201–1208.
- ICML-2006-KlaasBFDML #performance
- Fast particle smoothing: if I had a million particles (MK, MB, NdF, AD, SM, DL), pp. 481–488.