By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)
The e-book constitutes the complaints of the twenty fourth overseas convention on synthetic Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers incorporated within the complaints have been conscientiously reviewed and chosen from 173 submissions. the point of interest of the papers is on following issues: recurrent networks; aggressive studying and self-organisation; clustering and class; bushes and graphs; human-machine interplay; deep networks; idea; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical types and time sequence; neuroscience; and applications.
Read or Download Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings PDF
Best networks books
WiMAX is bringing a few around the world revolution in broadband instant entry, together with either fastened and cellular handsets. The IEEE 802. sixteen operating staff standardized such a lot features of WiMAX signaling messages. besides the fact that, a number of algorithms have been left unspecified beginning the door for thoughts in protocol engineering for 802.
This ebook constitutes the complaints of the 1st overseas meetings on e-Technologies and Networks for improvement, ICeND 2011, held in Dar-es-Salaam, Tanzania, in August 2011. The 29 revised complete papers offered have been conscientiously reviewed and chosen from ninety preliminary submissions. The papers deal with new advances within the web applied sciences, networking, e-learning, software program purposes, desktops, and electronic info and information communications applied sciences - besides technical as useful elements.
This e-book is a part of a 3 quantity set that constitutes the refereed court cases of the 4th foreign Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised lengthy papers and 192 revised brief papers awarded have been conscientiously reviewed and chosen from a complete of 1,975 submissions.
- Instructors Solution Manual to Artificial Neural Networks
- Space Division Multiple Access for Wireless Local Area Networks
- Mobile Networks for Biometric Data Analysis
- MotionCast for Mobile Wireless Networks
- Differential Forms on Electromagnetic Networks
Additional resources for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings
An interesting observation was also, that these two additional connection schemes provide a synergy eﬀect. Besides, we showed that DCMs just as LSTMs are able to generalize correctly over millions of time steps, which is, as far as we know, for the ﬁrst time reported in the literature. Next, we plan to investigate the DCM in more detail and study its performance on some real-world problems. References 1. : Evolving memory cell structures for sequence learning. , Ellinas, G. ) ICANN 2009, Part II.
Thus, not only the system speciﬁc parameters of the system of interest but also the crosssystem parameters may adjust unfavorably. By constraining the parameters of the system of interest to remain similar to those of the reference system, the cross-system parameters are less likely to be aﬀected by incomplete information about the system of interest. 5 Experiments We empirically assessed the eﬀectiveness of our regularization technique on the cart-pole  and mountain car  benchmarks. To be consistent with our previous work , we used the same settings for the cart-pole for training and evaluation and show the performance graph of the FTRNN that we obtained in the paper.
6 Conclusion We presented a regularization technique for the Factored Tensor Recurrent Neural Network (FTRNN) to learn the dynamics of an insuﬃciently observed system by exploiting the similarity to a well observed system in a dual-task learning approach. The FTRNN disentangles cross-system properties from peculiarities enabling to share knowledge eﬃciently among the systems. In previous work, we discovered that the parameters of the system of interest can converge to unfavorable values when information is insuﬃcient.
Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings by Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)