Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size.
<p>Fig 8 presents the mean absolute error (MAE) in millimeters as a function of kernel size (in pixels) for two model configurations. The red line, marked with circles, corresponds to the ‘Full Model’, while the blue line, marked with ‘X’ symbols, represents the ‘Reduced Depth’ version. The gr...
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2025
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| _version_ | 1852020365473087488 |
|---|---|
| author | Liu Liu (512237) |
| author2 | Zhifei Xu (540854) |
| author2_role | author |
| author_facet | Liu Liu (512237) Zhifei Xu (540854) |
| author_role | author |
| dc.creator.none.fl_str_mv | Liu Liu (512237) Zhifei Xu (540854) |
| dc.date.none.fl_str_mv | 2025-05-15T17:22:17Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320777.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_Mean_Absolute_Error_MAE_in_Millimeters_as_a_Function_of_Kernel_Size_/29079942 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Science Policy Mental Health Biological Sciences not elsewhere classified Information Systems not elsewhere classified agent &# 8217 neural plasticity mechanisms atari game settings various performance metrics standard atari games neuronal spike timings methodology leverages mrl including learning speed game generalization dependent plasticity xlink "> results show research explores learning efficiency hybrid mrl deep q changing conditions ai agents |
| dc.title.none.fl_str_mv | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Fig 8 presents the mean absolute error (MAE) in millimeters as a function of kernel size (in pixels) for two model configurations. The red line, marked with circles, corresponds to the ‘Full Model’, while the blue line, marked with ‘X’ symbols, represents the ‘Reduced Depth’ version. The graph tracks the performance of both models across kernel sizes ranging from 100 to 300 pixels. The Full Model generally maintains a lower MAE, suggesting higher accuracy than the Reduced Depth model. Both models show a decrease in MAE as the kernel size increases up to approximately 200 pixels. After this point, the Reduced Depth model’s MAE increases significantly, while the Full Model’s performance stabilizes before slightly increasing again.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e2b48906bc7dfdb853dc3f8cd24426cc |
| identifier_str_mv | 10.1371/journal.pone.0320777.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29079942 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size.Liu Liu (512237)Zhifei Xu (540854)NeuroscienceScience PolicyMental HealthBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedagent &# 8217neural plasticity mechanismsatari game settingsvarious performance metricsstandard atari gamesneuronal spike timingsmethodology leverages mrlincluding learning speedgame generalizationdependent plasticityxlink ">results showresearch exploreslearning efficiencyhybrid mrldeep qchanging conditionsai agents<p>Fig 8 presents the mean absolute error (MAE) in millimeters as a function of kernel size (in pixels) for two model configurations. The red line, marked with circles, corresponds to the ‘Full Model’, while the blue line, marked with ‘X’ symbols, represents the ‘Reduced Depth’ version. The graph tracks the performance of both models across kernel sizes ranging from 100 to 300 pixels. The Full Model generally maintains a lower MAE, suggesting higher accuracy than the Reduced Depth model. Both models show a decrease in MAE as the kernel size increases up to approximately 200 pixels. After this point, the Reduced Depth model’s MAE increases significantly, while the Full Model’s performance stabilizes before slightly increasing again.</p>2025-05-15T17:22:17ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0320777.g008https://figshare.com/articles/figure/Comparison_of_Mean_Absolute_Error_MAE_in_Millimeters_as_a_Function_of_Kernel_Size_/29079942CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290799422025-05-15T17:22:17Z |
| spellingShingle | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. Liu Liu (512237) Neuroscience Science Policy Mental Health Biological Sciences not elsewhere classified Information Systems not elsewhere classified agent &# 8217 neural plasticity mechanisms atari game settings various performance metrics standard atari games neuronal spike timings methodology leverages mrl including learning speed game generalization dependent plasticity xlink "> results show research explores learning efficiency hybrid mrl deep q changing conditions ai agents |
| status_str | publishedVersion |
| title | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| title_full | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| title_fullStr | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| title_full_unstemmed | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| title_short | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| title_sort | Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. |
| topic | Neuroscience Science Policy Mental Health Biological Sciences not elsewhere classified Information Systems not elsewhere classified agent &# 8217 neural plasticity mechanisms atari game settings various performance metrics standard atari games neuronal spike timings methodology leverages mrl including learning speed game generalization dependent plasticity xlink "> results show research explores learning efficiency hybrid mrl deep q changing conditions ai agents |