The full program can be obtained as a PDF file:
Also, the technical program and the venue maps are available online (browsable website) and for your mobile (iPhone & Android app) via Conference4me. You can get the Conference4me tool for free via the iTunes/Google Play store. Within the tool subscribe to ICANN2014.
Preliminary program overview
| 15.09.2014 | 16.09.2014 | 17.09.2014 | 18.09.2014 | 19.09.2014 | ||||||
| Registration | ||||||||||
| 9:00 | Keynote Paul Verschure |
Keynote Yann LeCun |
Keynote Kevin Gurney |
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| Opening Session | ||||||||||
| 10:00 | A1 | B1 | HMI1 | D1 | E1 | F1 | G1 | H1 | ||
| 11:00 | Coffee Break | Coffee Break | Coffee Break | Coffee Break | ||||||
| A2 | B2 | HMI2 | D2 | E2 | F2 | G2 | H2 | |||
| 12:00 | ||||||||||
| Lunch Break | Lunch Break | Lunch Break | Award and Closing Session | |||||||
| 13:00 | ||||||||||
| 14:00 | Keynote Christopher Bishop |
Keynote Jun Tani |
Keynote Barbara Hammer |
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| 15:00 | A3 | B3 | C3 | D3 | E3 | F3 | ||||
| 16:00 | Coffee Break | Coffee Break | Coffee Break | |||||||
| Poster Spot Talks | Poster Spot Talks | E4 | F4 | |||||||
| 17:00 | Poster and Demonstrations | Poster | ||||||||
| 18:00 | Registration | Welcome Reception | ENNS Board Meeting | |||||||
| 19:00 | Conference Dinner 19:00 – 23:00h |
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| 20:00 | ||||||||||
Keynotes, Poster Spot Talks and half of the sessions are in Lecture Hall 221.
Other sessions are in Lecture Hall 121. All rooms are in Building ESA1-West.
Wednesday will feature a special session on Human-Machine-Interaction chaired by Doreen Jirak in Lecture Hall 121. (Get details)
The sessions are entitled as follows:
| A1 | Recurrent Networks – Sequence Learning |
| A2 | Recurrent Networks – ESNs |
| A3 | Recurrent Networks – Theory |
| B1 | Competitive Learning and Self- Organisation |
| B2 | Clustering and Classification |
| B3 | Trees and Graphs |
| HMI1 | Human-Machine Interaction I |
| HMI2 | Human-Machine Interaction II |
| C3 | Deep Networks |
| D1 | Theory – Optimisation |
| D2 | Theory – Layered Networks |
| D3 | Reinforcement Learning and Action |
| E1 | Vision – Detection and Recognition |
| E2 | Vision – Invariances and Shape Recovery |
| E3 | Vision – Attention and Pose Estimation |
| E4 | Neuroscience – Cortical Models |
| F1 | Supervised Learning – Ensembles |
| F2 | Supervised Learning – Regression |
| F3 | Dynamical Models and Time Series |
| F4 | Supervised Learning – Classification |
| G1 | Neuroscience – Line Attractors and Neural Fields |
| G2 | Applications – Technical Systems |
| H1 | Applications – Users and Social Technologies |
| H2 | Neuroscience – Spiking and Single Cell Models |
