... of **Convolutional** Neural Networks (CNN), such as LeNet-5 [40] in arti- ficial **vision** tasks like hand-written digit classification or object recognition, stems from their architecture and inherent ...their ...

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... the **training** data while the second one is used as a test ...designed **for** static cameras and that videos of the intermittent object motion category does not fulfill our requirement about the ...

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... and **Deep** Learning that demonstrate how imposing a structure on large weight matrices can be used to reduce the size of the ...models **for** video classification based on state-of-the- art network ...

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... key **for** early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable ...multi-point **deep** ...

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... [3] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric **deep** learning on graphs and manifolds using mixture model cnns. In Pro- ceedings of the IEEE ...

2

... The **Convolutional** Neural Network The **Convolutional** Neural Network (CNN) has become recognized as the state of the art approach to many computer **vision** tasks including image-based object ...approach ...

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... a **Deep** Continuous Local Learning (DECOLLE) capable of learning **deep** spatio-temporal representations from spikes by approximating gradient backpropagation using locally syn- thesized ...loss **for** each ...

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... and **Vision** Computing, Elsevier, December 2018 Abstract In recent years, there has been rapid progress in solving the binary problems in computer **vision**, such as edge detection which finds the boundaries of ...

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... model **for** reading CAPTCHAs which factorize geometry and appearances, enabling the geometry and appearance to be learned separately, hence saving on **training** ...harder **for** a “black box” like a ...

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... same pool of 1.6M positive and negative Stanford data. Though this is not really a good approximation but it works quite well in the litteratures ([4], [11]). The loss of the precision is hoped to become less important ...

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... the **training**, the validation and the testing processes of ...accelerator **for** NNs, but it does not support variable network size and ...scalable **deep** learning accelerator unit on ...

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... Learning. **Training** the above CNN architecture is similar to the ...model. **For** faster convergence, the stochastic gradient descent (SGD) is used **for** updating the ...The **training** phase has two ...

9

... folds **for** **training** and one fold **for** ...RBCs **for** **training**, which we arrange in 50 batches of 20 images each except the last one that has only 8 RBCs; see Fig 19A ...layer-by-layer. ...

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... enables **training** by (possibly regularized) maximum likelihood and gradient descent computed via simple back-propagation, avoiding the need to compute intractable partition ...supervised **training** tricks. ...

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... allow **for** **training** of sufficiently large ...inspiration **for** connectionism, and view biological intelligence as a proof of concept giving some indication of what we can hope to achieve by simulating ...

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... position **for** each of the model units in “IT” layers (fc6 and fc7) on a two-dimensional artificial tissue map before **training**, simulating cortical maps in monkey IT (Figure ...high **for** nearby pairs ...

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... room **for** improvement when applying CNNs to ecological ...of **training** such **deep** **architectures**, the networks used by most studies implementing CNNs **for** image classification were ...

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... whereas **for** **training** 𝑇 1 𝐶 some peaks with a 30% error relative to the 𝑟 𝑚𝑠 density value are found before 𝜏 = ...harder **for** a network that has not seen those patterns during ...

19

... possibility **for** applications in many other ﬁelds, such as physics, where the causal nature of DL [7] suggests that complex patterns could also be sought and ...problems **for** which deterministic equations are ...

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... possibility **for** applications in many other ﬁelds, such as physics, where the causal nature of DL [7] suggests that complex patterns could also be sought and ...problems **for** which deterministic equations are ...

11