Summary of Calibration-then-calculation: a Variance Reduced Metric Framework in Deep Click-through Rate Prediction Models, by Yewen Fan et al.
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
by Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning has revolutionized many fields, but a crucial aspect often overlooked is evaluating the performance of deep learning pipelines. The usual practice is to train models once and compare them to previous benchmarks, which can lead to imprecise comparisons due to variance in neural network evaluation metrics caused by randomness in training. This limitation makes it difficult to detect effective modeling improvements. To address this issue, we propose a novel metric framework, the Calibrated Loss Metric, designed to reduce the variance present in its conventional counterpart. This new metric enhances accuracy in detecting effective modeling improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has made many things possible, but it’s important to make sure that these advancements are really making a difference. One way to do this is by evaluating how well deep learning models work. Right now, people usually train their models once and compare them to see which one does better. But this can be tricky because different models can perform differently just because of random chance. This makes it hard to tell if a new model is really an improvement or just got lucky. To solve this problem, scientists have come up with a new way to measure how well models do. They call it the Calibrated Loss Metric and it helps reduce the effects of randomness so we can get a more accurate idea of which models are truly better. |
Keywords
* Artificial intelligence * Deep learning * Neural network
