| J05-4005 | global minimum . We therefore use a | stochastic approximation | to gradient descent . Whereas |
| W12-3021 | sample . Since we are using a | stochastic approximation | to the model score , in general |
| W12-3021 | for Computational Linguistics a | stochastic approximation | . We propose two strategies for |
| N09-1069 | Stepwise EM is motivated from the | stochastic approximation | literature , where we think of |
| P15-1076 | training algorithm using joint | stochastic approximation | and trans-dimensional mixture |
| E06-1028 | simple " language model " , a | stochastic approximation | to the language of the collection |
| P15-1076 | similarly as in related works on | stochastic approximation | ( Gu and Zhu , 2001 ) and stochastic |
| D12-1101 | samples . Since we are using a | stochastic approximation | to the model score , in general |
| W13-3519 | that make either deterministic or | stochastic approximations | ( Kurihara and Sato , 2006 ; |
| P15-1076 | random field models . 3.2 Joint | stochastic approximation | Training random field models |
| J05-4005 | shown in Equation ( 7 ) , the | stochastic approximation | method updates parameters incrementally |
| D13-1160 | 0t +1 is set based on taking a | stochastic approximation | of aO ( B ; Bt ) B = Bt . aB |
| P07-1096 | updates at lines 13 and 14 are a | stochastic approximation | of gradient descent that minimizes |
| J05-4005 | − fd ( wi ) ) ( 8 ) The | stochastic approximation | method can be viewed as optimizing |
| N09-1069 | ηk . Standard results from the | stochastic approximation | literature state that E ∞ |
| P15-1076 | Model Estimation We develop a | stochastic approximation | algorithm using Markov chain |
