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Found 5 papers, 1 papers with code
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
This paper presents EffiBench, a benchmark with 1, 000 efficiency-critical coding problems for assessing the efficiency of code generated by code generation models.
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation
However, its application in code generation faces a distinct challenge, i. e., although the code generated with CoT reasoning is logically correct, it faces the problem of syntax error (e. g., invalid syntax error report) during code execution, which causes the CoT result's pass@1 in HumanEval even lower than the zero-shot result.
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing
To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets.
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Compared to all methods that do not use additional data for training, our models achieve 67. 3% and 41. 5% robust accuracy on CIFAR-10 and CIFAR-100 (improving upon the state-of-the-art by +7. 23% and +9. 07%).
Neural Architecture Search via Ensemble-based Knowledge Distillation
Neural Architecture Search (NAS) automatically searches for well-performed network architectures from a given search space.
