... LSTMs ( Hochreiter and Schmidhuber , 1997 ) employ the **multiscale** update con- cept, where the hidden units have different forget and update rates and thus can operate with different timescales. However, unlike our ...

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... 2 Institut Mines-T´el´ecom, T´el´ecom ParisTech, CNRS LTCI, 37-39 rue Dareau, 75014 Paris, France <firstname>.<lastname>@telecom-paristech.fr ABSTRACT In this paper, we propose a new method for singing voice ...

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... r(t) = f 2 (U r · r(t − 1) + w(t)); (2) where “+” represents element-wise addition. We set f 2 (.) to be the Rectified Linear Unit (ReLU), inspired by its the recent success when training very **deep** structure in ...

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... training **Recurrent** **Neural** **Networks** (Pas- canu, Mikolov, and Bengio, 2013), was published at the International Conference on Machine Learning (ICML) ...training **recurrent** models and provide ...

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... the **deep**-learning based approaches has outperformed the classical machine learning techniques such as Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and Logistic Regression (Badjatiya et ...

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... of **deep** learning and discussed some of the key conceptual elements and practices of contemporary **deep** learning ...of **deep** learning and representa- tion learning, and their relevance to the goals of ...

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... The second article, titled “**Recurrent** **Neural** **Networks** for Emotion Recognition in Video” (Ebrahimi Kahou et al., 2015), addresses the shortcomings of the previous article. Specifically, it introduces ...

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... tried **neural** net- works for sentiment classification ...cation. **Neural** network models and automatically learned word vector features came together to achieve state-of-the-art results on sentiment ...

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... Training **Recurrent** **Networks** ...optimizing **deep** **networks** is that in ordinary **neural** **networks** gradients diffuse through the layers, diffusing credit and blame through many units, ...

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... Fig. 3: One enhancement iteration represented as common **neural** network layers. Features are extracted both from the input image I and the heat map of the previous iteration ut. These are then concatenated and ...

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... **networks** on toy ...in **neural** **networks** also has biological ...biologically-plausible **deep** network that allows one to construct richer and more versatile representations using complex-valued ...

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... state-of-the-art **deep** learning on sequential ...in **recurrent** **networks**, and prove that it mitigates the problem of vanishing gradients when trying to capture long-term ...

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... Due to the massive rise of hateful, abusive, offen- sive messages, social media platforms such as Twit- ter and Facebook have been searching for solutions to tackle hate speech (Lomas, 2016). As a conse- quence, the ...

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... tificial **neural** **networks** (NNs) limited the number of parameters that could be estimated and did not scale to the size of real seismic ...data. **Deep** learning allows the application of NNs to much more ...

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... by the CHiME-3 challenge organizers 3 [40], [56]. The evalua- tion includes the uses of (a) feature-space maximum likelihood regression (fMLLR) features [57]; (b) acoustic models based on Gaussian Mixture Model (GMM) ...

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... The previous subsection discussed the status of at- tempts to create spiking versions of LSTMs. Rather than pursuing a direct approach to structurally trans- lating an LSTM to a spiking version, the work of [228] took a ...

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... In **deep** learning and numerical optimization literature, several papers suggest using a diagonal approximation of the Hessian (second derivative matrix of the cost function with respect to parameters), in order to ...

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... use **recurrent** **deep** **neural** **networks** or DRNN (**Deep** **Recurrent** **Neural** Network) to classify the manoeuvres of an enemy ...

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... residual-learning **networks** are trained from scratch using Kirby 21 with Adam optimization over 20 epochs and tested with the testing images of the same dataset for isotropic scale factor ...

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... Figure 4-9: Train accuracies on the Linear/Quadratic Dataset. The training accuracy grows for the L points, which require a simpler classifier, first. 4.4.2 The Simplicity Bias: A Proof of Concept As discussed earlier, ...

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