We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| geifmany/Active-inas | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| inas | Ran | geifmany/Active-inas/active_inas.py code served (permissive licence) · get_code("53812ab2d5b57918") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1811.07579")
get_code_for_paper("1811.07579")
have("1811.07579")
Connect an agent — have() is free.