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 |
|---|---|---|
| HazyResearch/metal | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| probs_to_preds | Ran | HazyResearch/metal/metal/mmtl/metal_model.py code served (permissive licence) · get_code("abb7b213e9af89b0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1706.05098")
get_code_for_paper("1706.05098")
have("1706.05098")
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