新たなRNNと自然言語処理12. • • • • • • 12 [Jozefowicz+15] ) . (18) T recurrence +1) ) C +1) (19) the model) aining RNN- (20) 1)T (21) )T . (22) ormance of wo baseline tion capture mean frame- of compari- RBM is per- ling starting ptimally. d on a simu- er & Hinton, Figure 3. Receptive fields of 48 hidden units of an RNN- RBM trained on the bouncing balls dataset. Each square shows the input weights of a hidden unit as an
