It is not common to solve this problem as a simple unconstrained optimization problem with gradient descent. For that reason, some papers that meet the criteria may not be accepted while others can be. Least squares generative adversarial networks (2016), X. Mao et al. It depends on the impact of the paper, applicability to other researches scarcity of the research domain, and so on. Regression is the task of predicting real-valued quantities, such as the price of houses or the length of something in an image. But what if \(y_i\) is a binary vector where every example may or may not have a certain attribute, and where the attributes are not exclusive? instead using \(\max(0, f_j - f_{y_i} + 1)^2\)). To get the news for newly released papers everyday, follow my twitter or facebook page! An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al. Furthermore, the covariance matrix is symmetric and positive semi-definite. This gives the initialization w = np.random.randn(n) / sqrt(n). Those most relevant to this paper include: Box-constrained L-BFGS can reliably find adversarial examples. Additionally, this has the appealing property that the prediction code can remain untouched when you decide to tweak where you apply dropout, or if at all. In case of images, the relative scales of pixels are already approximately equal (and in range from 0 to 255), so it is not strictly necessary to perform this additional preprocessing step. High-entropy alloys, with N elements and compositions {cν = 1,N} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. al. Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015), C. Dyer et al. There are two common ways of achieving this normalization. That is, for every weight \(w\) in the network, we add the term \(\frac{1}{2} \lambda w^2\) to the objective, where \(\lambda\) is the regularization strength. al. Adversarially learned inference (2016), V. Dumoulin et al. (2016), J. Hosang et al. For this task, it is common to compute the loss between the predicted quantity and the true answer and then measure the L2 squared norm, or L1 norm of the difference. But the seminal paper establishing the modern subject of convolutional networks was a 1998 paper, "Gradient-based learning applied to document recognition" , by Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains. In practice, this corresponds to performing the parameter update as normal, and then enforcing the constraint by clamping the weight vector \(\vec{w}\) of every neuron to satisfy \(\Vert \vec{w} \Vert_2 < c\). Typical values of \(c\) are on orders of 3 or 4. Common pitfall. In third step we assumed zero mean inputs and weights, so \(E[x_i] = E[w_i] = 0\). Find out how by reading the rest of this post. Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. Mean subtraction is the most common form of preprocessing. Describing videos by exploiting temporal structure (2015), L. Yao et al. There are several ways of controlling the capacity of Neural Networks to prevent overfitting: L2 regularization is perhaps the most common form of regularization. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al. Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al. There are three common forms of data preprocessing a data matrix X, where we will assume that X is of size [N x D] (N is the number of data, D is their dimensionality). (Thus, removing papers is also important contributions as well as adding papers), Papers that are important, but failed to be included in the list, will be listed in. During testing, the noise is marginalized over analytically (as is the case with dropout when multiplying by \(p\)), or numerically (e.g. Dropout falls into a more general category of methods that introduce stochastic behavior in the forward pass of the network. Learning deep architectures for AI (2009), Y. Bengio. Learning to discover cross-domain relations with generative adversarial networks (2017), T. Kim et al. Convolutional Sequence to Sequence Learning (2017), Jonas Gehring et al. End-to-end memory networks (2015), S. Sukbaatar et al. Lastly, notice that during gradient descent parameter update, using the L2 regularization ultimately means that every weight is decayed linearly: W += -lambda * W towards zero. Intuitively, it requires a very fragile and specific property from the network to output exactly one correct value for each input (and its augmentations). Additionally, the L2 loss is less robust because outliers can introduce huge gradients. Are you talking to a machine? Small random numbers. Deep Photo Style Transfer (2017), F. Luan et al. One of two most commonly seen cost functions in this setting is the SVM (e.g. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al. What makes for effective detection proposals? You signed in with another tab or window. (Update) You can download all top-100 papers with this and collect all authors' names with this. A neural conversational model (2015), O. Vinyals and Q. Dean et al. The projection therefore corresponds to a rotation of the data in X so that the new axes are the eigenvectors. On the properties of neural machine translation: Encoder-decoder approaches (2014), K. Cho et. Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. The backward pass remains unchanged, but of course has to take into account the generated masks U1,U2. An example of other research in this direction includes DropConnect, where a random set of weights is instead set to zero during forward pass. TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al. Neat! The basic idea behind the structured SVM loss is to demand a margin between the correct structure \(y_i\) and the highest-scoring incorrect structure. Normalization refers to normalizing the data dimensions so that they are of approximately the same scale. Note that we’re adding 1e-5 (or a small constant) to prevent division by zero. FIGURE 6.3: Adversarial examples for AlexNet by Szegedy et. Continuous deep q-learning with model-based acceleration (2016), S. Gu et al. The training set of CIFAR-10 is of size 50,000 x 3072, where every image is stretched out into a 3072-dimensional row vector. In particular, a Neural Network performs a sequence of linear mappings with interwoven non-linearities. Deep Neural Networks Motivated by Partial Differential Equations; In this lecture we will continue to relate the methods of machine learning to those in scientific computing by looking at the relationship between convolutional neural networks and partial differential equations. Notice that loss is accumulated if a positive example has score less than +1, or when a negative example has score greater than -1. We can then compute the [3072 x 3072] covariance matrix and compute its SVD decomposition (which can be relatively expensive). Large scale distributed deep networks (2012), J. The implementation for one weight matrix might look like W = 0.01* np.random.randn(D,H), where randn samples from a zero mean, unit standard deviation gaussian. In practice networks that use Batch Normalization are significantly more robust to bad initialization. Bag of tricks for efficient text classification (2016), A. Joulin et al. Region-based convolutional networks for accurate object detection and segmentation (2016), R. Girshick et al. Recurrent neural network regularization (2014), W. Zaremba et al. For example, in case of \(p = 0.5\), the neurons must halve their outputs at test time to have the same output as they had during training time (in expectation). Another form of this preprocessing normalizes each dimension so that the min and max along the dimension is -1 and 1 respectively. Finding function in form: Compositional character models for open vocabulary word representation (2015), W. Ling et al. Thank you for all your contributions. Common data preprocessing pipeline. You can convince yourself that this simplifies to minimizing the negative log-likelihood: where the labels \(y_{ij}\) are assumed to be either 1 (positive) or 0 (negative), and \(\sigma(\cdot)\) is the sigmoid function. This is also sometimes refereed to as Principal Component Analysis (PCA) dimensionality reduction: After this operation, we would have reduced the original dataset of size [N x D] to one of size [N x 100], keeping the 100 dimensions of the data that contain the most variance. The core observation is that this is possible because normalization is a simple differentiable operation. From captions to visual concepts and back (2015), H. Fang et al. CAS PubMed PubMed Central Article Google Scholar Theano: A Python framework for fast computation of mathematical expressions, R. Al-Rfou et al. Long short-term memory (1997), S. Hochreiter and J. Schmidhuber. It is not very common to regularize different layers to different amounts (except perhaps the output layer). With images specifically, for convenience it can be common to subtract a single value from all pixels (e.g. A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al. We have seen how to construct a Neural Network architecture, and how to preprocess the data. Biography Jiebo Luo joined the University of Rochester in Fall 2011 after over fifteen prolific years at Kodak Research Laboratories, where he was a Senior Principal Scientist leading research and advanced development.He has been involved in numerous technical conferences, including serving as the program co-chair of ACM Multimedia 2010, IEEE CVPR 2012 and IEEE ICIP 2017. An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. Le. Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. When faced with a regression task, first consider if it is absolutely necessary. We’ve now preprocessed the data and set up and initialized the model. Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al. On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj. Why does unsupervised pre-training help deep learning (2010), D. Erhan et al. In other words, there is no source of asymmetry between neurons if their weights are initialized to be the same. It is also possible to use small numbers drawn from a uniform distribution, but this seems to have relatively little impact on the final performance in practice. # Assume input data matrix X of size [N x D], # whiten the data: If we were to compute the covariance matrix of Xrot, we would see that it is now diagonal. Learning to compose neural networks for question answering (2016), J. Andreas et al. Levy et al. Adaptive computation time for recurrent neural networks (2016), A. Graves. There are several types of problems you might want to solve in practice: Classification is the case that we have so far discussed at length. Deep sparse rectifier neural networks (2011), X. Glorot et al. Also, bib file for all top-100 papers are available. Szegedy et al. Intriguing properties of neural networks. Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al. Thanks, doodhwala, Sven and grepinsight! Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. 22 , 288–304 (2012). Left: Original toy, 2-dimensional input data.Middle: The data is zero-centered by subtracting the mean in each dimension.The data cloud is now centered around the origin. It involves subtracting the mean across every individual feature in the data, and has the geometric interpretation of centering the cloud of data around the origin along every dimension. Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. The L2 regularization has the intuitive interpretation of heavily penalizing peaky weight vectors and preferring diffuse weight vectors. As foreshadowing, Convolutional Neural Networks also take advantage of this theme with methods such as stochastic pooling, fractional pooling, and data augmentation. Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al. From this derivation we can see that if we want \(s\) to have the same variance as all of its inputs \(x\), then during initialization we should make sure that the variance of every weight \(w\) is \(1/n\). Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al. An image might help: In practice. Intriguing properties of neural networks (2014), C. Szegedy et al. It is possible to combine the L1 regularization with the L2 regularization: \(\lambda_1 \mid w \mid + \lambda_2 w^2\) (this is called Elastic net regularization). Beyond short snippents: Deep networks for video classification (2015). in numpy: Use L2 regularization and dropout (the inverted version), We discussed different tasks you might want to perform in practice, and the most common loss functions for each task. The results add to the growing body of evidence for a contribution of the cerebellum to functions previously ascribed to the forebrain, and provide major new insights about cellular and circuit level mechanisms for this contribution. Natural language processing (almost) from scratch (2011), R. Collobert et al. Bias regularization. It is common to see the factor of \(\frac{1}{2}\) in front because then the gradient of this term with respect to the parameter \(w\) is simply \(\lambda w\) instead of \(2 \lambda w\). Recurrent models of visual attention (2014), V. Mnih et al. As a solution, it is common to initialize the weights of the neurons to small numbers and refer to doing so as symmetry breaking. In other words, neurons with L1 regularization end up using only a sparse subset of their most important inputs and become nearly invariant to the “noisy” inputs. We drop and scale at train time and don't do anything at test time. When faced with a regression problem, first consider if it is absolutely inadequate to quantize the output into bins. Relatively few results regarding this idea have been published in the literature.
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