Name
Abstract | ||
|---|---|---|
We propose a new learning paradigm called Deep Memory. It has the potential to completely revolutionize the Machine Learning field. Surprisingly, this paradigm has not been reinvented yet, unlike Deep Learning. At the core of this approach is the textit{Learning By Heart} principle, well studied in primary schools all over the world. Inspired by poem recitation, or by $pi$ decimal memorization, we propose a concrete algorithm that mimics human behavior. We implement this paradigm on the task of generative modeling, and apply to images, natural language and even the $pi$ decimals as long as one can print them as text. The proposed algorithm even generated this paper, in a one-shot learning setting. In carefully designed experiments, we show that the generated samples are indistinguishable from the training examples, as measured by any statistical tests or metrics. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Generative modeling,Natural language,Artificial intelligence,Deep learning,Decimal,Statistical hypothesis testing,Memorization,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1803.11203 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Gelly | 1 | 760 | 59.74 |
| Karol Kurach | 2 | 234 | 13.37 |
| Marcin Michalski | 3 | 22 | 4.00 |
| Xiaohua Zhai | 4 | 209 | 13.00 |