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end-to-end memNN ...(1,770 × 634 (191 KB)) - 13:03, 7 November 2017
...roposed EM-CNN-LR method achieved the best result (accuracy: 0.771), close to inter-observer agreement between pathologists. ...(836 × 934 (465 KB)) - 10:19, 18 November 2021
...s a function of the proportion p in class 2. Cross-entropy has been scaled to pass through(0.5,0.5) ...(422 × 221 (14 KB)) - 19:01, 10 November 2011
Alejandro Newell and Jia Deng. Associative embedding: End-to-end learning for joint detection and grouping. arXiv preprint arXiv:1611.05 ...(545 × 261 (41 KB)) - 23:21, 2 November 2018
Alejandro Newell and Jia Deng. Associative embedding: End-to-end learning for joint detection and grouping. arXiv preprint arXiv:1611.05 ...(545 × 261 (41 KB)) - 23:26, 2 November 2018
A general Boltzmann Machine. All units are connected to all others. 4 units in the visible layer and 4 in the hidden layer. ...(397 × 220 (31 KB)) - 12:48, 18 November 2011
...sparsity aware algorithm on Allstate-10K. The dataset is sparse mainly due to one-hot encoding. The sparsity aware algorithm is more than 50 times faster ...(1,366 × 780 (101 KB)) - 11:03, 17 March 2018
...training neural network language models with maximum entropy models leads to better performance in terms of computational complexity. ...e state in the previous time step. This recurrence allows the hidden layer to represent low-dimensional representation of the entire history. ...(625 × 315 (13 KB)) - 01:01, 20 November 2015
...rrent and goal observations and outputs an action sequence that would lead to that goal. We compare the performance of the following GSP models: (a) Simp ...(3,892 × 1,976 (382 KB)) - 23:45, 31 October 2018
