Name
Abstract | ||
|---|---|---|
Big data can be a blessing: with very large training data sets it becomes possible to perform complex learning tasks with unprecedented accuracy. Yet, this improved performance comes at the price of enormous computational challenges. Thus, one may wonder: Is it possible to leverage the information content of huge data sets while keeping computational resources under control? Can this also help sol... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MSP.2021.3092574 | IEEE Signal Processing Magazine |
DocType | Volume | Issue |
Journal | 38 | 5 |
ISSN | Citations | PageRank |
1053-5888 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rémi Gribonval | 1 | 1207 | 83.59 |
| Antoine Chatalic | 2 | 1 | 0.70 |
| Nicolas Keriven | 3 | 1 | 0.36 |
| Vincent Schellekens | 4 | 1 | 0.36 |
| Laurent Jacques | 5 | 538 | 41.92 |
| Philip Schniter | 6 | 1620 | 93.74 |