图 书 馆
神经程序解释器Neural Programmer-Interpreters |
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| 课程网址: | http://videolectures.net/iclr2016_reed_neural_programmer/ |
| 主讲教师: | Scott Reed |
| 开课单位: | 视频讲座网 |
| 开课时间: | 2016-05-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms. |
| 关 键 词: | 神经网络; 循环核心; 程序存储 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-02：chenxin01 |
| 最后编审: | 2022-12-02：chenxin01 |
| 阅读次数: | 38 |