RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang ICLR 2019 Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul ACL 2019 Probabilistic...
Search for the usage of a specific API in theAPI reference manual, which organizes all DGL APIs by their namespace. All the learning materials are available at ourdocumentation site. If you are new to deep learning in general, check out the open source bookDive into Deep Learning. ...
Even though the sparse image does not contain the vessels, they are restored in the DNN localization-based image because our network is based on 3D convolutions, allowing for the reference of adjacent pixels in 3D space. These results prove that our DL-based framework can reconstruct a dense ...
Additionally, we present two measures that characterize the space efficiency of our approach. The compression rate express the percentage reduction in space requirements for representing an animation sequence with an LBS scheme as opposed to store the entire mesh sequence. For the computations, we use...
Furthermore, ESN hyperparameters, such as the number of units in the reservoir and input scaling, are limited to a short operating space that ensures maximum model performance [176]. 4 Deep Fuzzy Regression Deep fuzzy systems (DFSs) are models built on top of DL structures with fuzzy logic ...
(b) Sclera (SHG) was rendered transparent to reveal the relationships of ISPs with CC (yellow), collector channel openings (CCO; dashed circle), SC (gap) and TM (green) in 3D space. (c) The 3D model is rotated, showing a trans-scleral view from the ocular surface of interconnected ...
San Diego: Academic Press, 1995. Pp. vii+255, illus. ISBN 0-12-515175-6. $64.95. DOUGLAS J. MUDGWAY, Uplink-Downlink: A History of the Deep Space Network 1957–1997. NASA History Series. Washington, DC: National Aeronautics and ... J Agar - 《British Journal for the History of ...
Thus, they also have advantage of saving disk space42. Figure 3 shows some typical augmented images where the amount of the training data is enhanced arbitrarily by generating an altered image version. The methods used for the augmentation are shifts, rotations, shear, brightness, and zooming. ...
Since the DNN is first exposed to a large amount of data, its parameters will be carried to a space that is more likely to represent the data distribution in overall rather than over-fitting a specific subset of underlying data distribution52,53. Secondly, pre-training can improve the ...