Name
Abstract | ||
|---|---|---|
Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of \"one-shot learning.\" Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms. |
Year | Venue | Field |
|---|---|---|
2016 | ICML | Nervous system network models,Physical neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Artificial neural network,Machine learning,Mathematics |
DocType | Citations | PageRank |
Conference | 64 | 1.90 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Santoro | 1 | 438 | 20.37 |
| Sergey Bartunov | 2 | 148 | 6.64 |
| Matthew M Botvinick | 3 | 494 | 25.34 |
| Daan Wierstra | 4 | 5412 | 255.92 |
| Timothy P. Lillicrap | 5 | 4377 | 170.65 |