Ye Tian, Shangshang Yang, Xiaoshan Yu, Xueming Yan, Haiping Ma, Xingyi Zhang
We lifted 14 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| devilyangs/enas-kt | canonical | 7 of 10 |
| DevilYangS/ENAS-KT | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| F_distance | Ran | devilyangs/enas-kt/EMO_public/F_distance.py pointer only (licence: NONE) · get_code("a8be0cbff1e83846") |
| F_mating | Ran | devilyangs/enas-kt/EMO_public/F_mating.py pointer only (licence: NONE) · get_code("45641417c526d625") |
| Get_auc | Ran | devilyangs/enas-kt/Operations.py pointer only (licence: NONE) · get_code("49878a238817e447") |
| evaluation | Ran | DevilYangS/ENAS-KT/EvoTransformer.py pointer only (licence: NONE) · get_code("991bdeb73a94fdd8") |
| get_Ednet_training_config | Ran | devilyangs/enas-kt/config.py pointer only (licence: NONE) · get_code("eb6d7e95eac06538") |
| get_metrics | Ran | devilyangs/enas-kt/utils.py pointer only (licence: NONE) · get_code("73aac25ea9f0d376") |
| get_optimal_value | Ran | devilyangs/enas-kt/utils.py pointer only (licence: NONE) · get_code("e0840c2d2f3aea15") |
| train | Ran | DevilYangS/ENAS-KT/training_script.py pointer only (licence: NONE) · get_code("4b91e5408023e9ff") |
| train_super | Ran | DevilYangS/ENAS-KT/training_script.py pointer only (licence: NONE) · get_code("ca11fb11243c1b65") |
| trunc_normal_ | Ran | devilyangs/enas-kt/model/Transformer_super_V3.py pointer only (licence: NONE) · get_code("a7e7d4147dcf0849") |
| get_Average_optimal_effect | Not yet run | devilyangs/enas-kt/utils.py pointer only (licence: NONE) · get_code("b17a27d37109ab1c") |
| get_concat_mask | Not yet run | devilyangs/enas-kt/Operations.py pointer only (licence: NONE) · get_code("b8317c399e2303d7") |
| get_mask | Not yet run | devilyangs/enas-kt/Operations.py pointer only (licence: NONE) · get_code("465402206925c3af") |
| train_student | Not yet run | DevilYangS/ENAS-KT/training_script.py pointer only (licence: NONE) · get_code("f8ed97f839035f8f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Transformer has achieved excellent performance in the knowledge tracing (KT) task, but they are criticized for the manually selected input features for fusion and the defect of single global context modelling to directly capture students' forgetting behavior in KT, when the related records are distant from the current record in terms of time. To address the issues, this paper first considers adding convolution operations to the Transformer to enhance its local context modelling ability used for students' forgetting behavior, then proposes an evolutionary neural architecture search approach to automate the input feature selection and automatically determine where to apply which operation for achieving the balancing of the local/global context modelling. In the search space design, the original global path containing the attention module in Transformer is replaced with the sum of a global path and a local path that could contain different convolutions, and the selection of input features is also considered. To search the best architecture, we employ an effective evolutionary algorithm to explore the search space and also suggest a search space reduction strategy to accelerate the convergence of the algorithm. Experimental results on the two largest and most challenging education datasets demonstrate the effectiveness of the architecture found by the proposed approach.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.01180")
get_code_for_paper("2310.01180")
have("2310.01180")
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