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Date Name Thumbnail Size User Description Versions
00:43, 23 November 2021 table2.png (file) 26 KB V23joshi   3
00:43, 23 November 2021 table1.png (file) 20 KB V23joshi   8
00:25, 23 November 2021 formula.png (file) 8 KB V23joshi   2
00:25, 23 November 2021 final.png (file) 5 KB V23joshi   1
00:24, 23 November 2021 hyperplane.png (file) 17 KB V23joshi   1
00:24, 23 November 2021 Figure2.png (file) 64 KB Slngew   4
00:23, 23 November 2021 Table3.png (file) 136 KB Slngew   1
00:23, 23 November 2021 Table2.png (file) 116 KB Slngew   2
00:22, 23 November 2021 Table1.png (file) 154 KB Slngew   9
15:58, 22 November 2021 Picture3.png (file) 83 KB Y2268che   1
15:58, 22 November 2021 Picture2.png (file) 113 KB Y2268che   2
15:58, 22 November 2021 Picture1.png (file) 54 KB Y2268che   2
11:07, 20 November 2021 hypothesis.png (file) 83 KB B349zhan   1
10:50, 20 November 2021 definition.png (file) 83 KB B349zhan   1
15:09, 18 November 2021 Catboost tree algorithm.png (file) 66 KB Ylooi   1
14:19, 18 November 2021 Ordered boosting principle.png (file) 22 KB Ylooi   1
14:12, 18 November 2021 SampleWiseDoubleDescent.png (file) 236 KB Sessenko   1
14:05, 18 November 2021 EpochWiseDoubleDescent.png (file) 255 KB Sessenko   1
13:57, 18 November 2021 ModelWiseDoubleDescent.png (file) 220 KB Sessenko   1
10:17, 18 November 2021 NSCLC classification results.png (file) 149 KB C296wong   1
09:19, 18 November 2021 Glioma classification results.png (file) 465 KB C296wong The figure summarizes the Glioma classification results. The proposed EM-CNN-LR method achieved the best result (accuracy: 0.771), close to inter-observer agreement between pathologists. 1
03:00, 16 November 2021 441 14 scale2.png (file) 45 KB Jtfan   1
02:59, 16 November 2021 441 14 scale1.png (file) 65 KB Jtfan   1
02:58, 16 November 2021 441 14 spatial.png (file) 80 KB Jtfan   1
02:54, 16 November 2021 441 14 fov2.png (file) 123 KB Jtfan   1
02:54, 16 November 2021 441 14 fov1.png (file) 102 KB Jtfan   1
02:52, 16 November 2021 441 14 rep2.png (file) 325 KB Jtfan   1
02:52, 16 November 2021 441 14 rep1.png (file) 602 KB Jtfan   1
02:44, 16 November 2021 441 14 CKA.PNG (file) 9 KB Jtfan   1
02:36, 16 November 2021 441 14 self-attention.PNG (file) 30 KB Jtfan   1
02:32, 16 November 2021 441 14 transformer.jpg (file) 65 KB Jtfan   1
02:31, 16 November 2021 441 14 resnet.png (file) 28 KB Jtfan   1
20:21, 15 November 2021 Whole Slide Tissue Image of a grade IV tumor.png (file) 402 KB Alivochk   1
18:58, 15 November 2021 stat441 f21 group10 masked layer.jpg (file) 35 KB Eofarrel   1
18:39, 15 November 2021 fig2 model architecture.jpeg (file) 239 KB Alivochk Fig. 2., Hou, Le & Samaras, Dimitris & Kurc, Tahsin & Gao, Yi & Davis, James & Saltz, Joel. (2016). Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification. Proceedings. IEEE Computer Society Conference on Computer Vision a... 1
18:05, 15 November 2021 stat441 f21 group10 pruning synapse vs neuron.png (file) 49 KB Eofarrel   1
01:27, 15 November 2021 Line graphs.png (file) 234 KB Y37duan   1
01:25, 15 November 2021 Mean error result.png (file) 111 KB Y37duan   1
01:21, 15 November 2021 Hurricane predictions.png (file) 409 KB Y37duan   1
01:14, 15 November 2021 Model example.png (file) 503 KB Y37duan   1
23:37, 13 November 2021 Number of attributes.png (file) 37 KB Y2454liu   1
23:36, 13 November 2021 soft sharing.png (file) 32 KB Y2454liu   1
23:33, 13 November 2021 classification accuracy.png (file) 63 KB Y2454liu   1
08:33, 13 November 2021 The proposed soft-sharing approach.png (file) 47 KB Y2454liu   1
08:28, 13 November 2021 ResNet-26 with depthwise separable convolution.png (file) 111 KB Y2454liu   2
08:20, 13 November 2021 Standard convolution and depthwise separable convolution.png (file) 28 KB Y2454liu   2
23:15, 11 November 2021 DJB CIFAR10.png (file) 183 KB M274xu   1
23:12, 11 November 2021 simulation.png (file) 251 KB M274xu   1
16:22, 11 November 2021 model arch3.png (file) 530 KB Kdchopra   1
16:11, 11 November 2021 model arch2.png (file) 74 KB Kdchopra   1
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