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TNN
2008
177views more  TNN 2008»
14 years 11 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
NIPS
1993
15 years 1 months ago
Credit Assignment through Time: Alternatives to Backpropagation
Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networ...
Yoshua Bengio, Paolo Frasconi
IWANN
1999
Springer
15 years 4 months ago
Forecasting Financial Time Series through Intrinsic Dimension Estimation and Non-Linear Data Projection
A crucial problem in non-linear time series forecasting is to determine its auto-regressive order, in particular when the prediction method is non-linear. We show in this paper tha...
Michel Verleysen, Eric de Bodt, Amaury Lendasse
NECO
2007
258views more  NECO 2007»
14 years 11 months ago
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spiketiming-dependent plasticity (...
Razvan V. Florian
IJCNN
2000
IEEE
15 years 4 months ago
Applying CMAC-Based On-Line Learning to Intrusion Detection
The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existin...
James Cannady