Travelled to:1 × China
1 × France
1 × United Kingdom
2 × USA
Collaborated with:R.S.Zemel I.Sutskever J.Snoek R.P.Adams Y.Li J.Martens D.Tarlow L.Charlin Y.Wu T.Pitassi C.Dwork M.Ranzato D.Buchman B.M.Marlin N.d.Freitas O.Rippel R.Kiros N.Satish N.Sundaram M.M.A.Patwary Prabhat
Talks about:bayesian (2) network (2) optim (2) match (2) learn (2) neighborhood (1) unsupervis (1) stationari (1) represent (1) autoencod (1)
Person: Kevin Swersky
DBLP: Swersky:Kevin
Contributed to:
20152014201320122011Wrote 7 papers:
- ICML-2015-LiSZ #generative #network
- Generative Moment Matching Networks (YL, KS, RSZ), pp. 1718–1727.
- ICML-2015-SnoekRSKSSPPA #network #optimisation #scalability #using
- Scalable Bayesian Optimization Using Deep Neural Networks (JS, OR, KS, RK, NS, NS, MMAP, P, RPA), pp. 2171–2180.
- ICML-c2-2014-SnoekSZA #optimisation
- Input Warping for Bayesian Optimization of Non-Stationary Functions (JS, KS, RSZ, RPA), pp. 1674–1682.
- ICML-c3-2013-TarlowSCSZ #learning #probability
- Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (DT, KS, LC, IS, RSZ), pp. 199–207.
- ICML-c3-2013-ZemelWSPD #learning
- Learning Fair Representations (RSZ, YW, KS, TP, CD), pp. 325–333.
- ICML-2012-MartensSS
- Estimating the Hessian by Back-propagating Curvature (JM, IS, KS), p. 126.
- ICML-2011-SwerskyRBMF #energy #modelling #on the
- On Autoencoders and Score Matching for Energy Based Models (KS, MR, DB, BMM, NdF), pp. 1201–1208.