| P01-1005 | , sample selection outperforms | sequential sampling | . At the endpoint of each training |
| P10-1037 | active learning . Following their | sequential sampling | algorithm , we show in Figure |
| W04-3202 | a much stronger baseline than | sequential sampling | for the Redwoods corpus ( Osborne |
| W04-3202 | sampling . This performed better than | sequential sampling | but was only half as effective |
| D10-1034 | are included for comparison : | sequential sampling | ( SEQ ) , which selects a sequentially-occurring |
| W14-2001 | time-delayed foveal inhibition over a | sequential sampling | process , but we note that spillover |
