The idea is that the neurons are all random and unique in the beginning, so they will compute distinct updates and integrate themselves as diverse parts of the full network. 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. (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. Learning spatiotemporal features with 3d convolutional networks (2015), D. Tran et al. However, in practical applications (and with proper data preprocessing) regularizing the bias rarely leads to significantly worse performance. 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 \(\Vert \vec{w} \Vert_2 < c\). Dean et al. Recurrent neural network regularization (2014), W. Zaremba et al. Lets start with what we should not do. Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Achilleos, A. Thank you for all your contributions. very close to exactly zero). CAS PubMed PubMed Central Article Google Scholar Relatively few results regarding this idea have been published in the literature. (2014b) demonstrated a variety of intriguing properties of neural networks and related models. This is important because at test time all neurons see all their inputs, so we want the outputs of neurons at test time to be identical to their expected outputs at training time. Why does unsupervised pre-training help deep learning (2010), D. Erhan et al. Vanilla dropout in an example 3-layer Neural Network would be implemented as follows: In the code above, inside the train_step function we have performed dropout twice: on the first hidden layer and on the second hidden layer. The data loss takes the form of an average over the data losses for every individual example. [[pdf]] (. Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches. A similar analysis is carried out in Understanding the difficulty of training deep feedforward neural networks by Glorot et al. Large scale distributed deep networks (2012), J. al. Recommended further reading for an interested reader includes: Theme of noise in forward pass. 22 , 288–304 (2012). Understanding neural networks through deep visualization (2015), J. Yosinski et al. PCA and Whitening is another form of preprocessing. Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017), T. Salimans et al. This can in practice be mitigated by stronger smoothing (i.e. E.g. QM/MM Investigation of the Spectroscopic Properties of the Fluorophore of Bacterial Luciferase. (Update) You can download all top-100 papers with this and collect all authors' names with this. SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016), Rajpurkar et al. 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. Neural networks are, generally speaking, differentiable with respect to their inputs. The structure of the tree strongly impacts the performance and is generally problem-dependent. With images specifically, for convenience it can be common to subtract a single value from all pixels (e.g. Gradient-based learning applied to document recognition (1998), Y. LeCun et al. Small random numbers. al (2013). The undesirable property of the scheme presented above is that we must scale the activations by \(p\) at test time. 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. At first, this limit may seem impractical and even pointless to study. Bias regularization. In practice, if you are not concerned with explicit feature selection, L2 regularization can be expected to give superior performance over L1. Conditional image generation with pixelcnn decoders (2016), A. van den Oord et al. A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al. 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. computing the mean and subtracting it from every image across the entire dataset and then splitting the data into train/val/test splits would be a mistake. A reasonable-sounding idea then might be to set all the initial weights to zero, which we expect to be the “best guess” in expectation. This point was further argued in Intriguing properties of neural networks by Szegedy et al., where they perform a similar visualization along arbitrary directions in the representation space. TACOTRON: Towards end-to-end speech synthesis (2017), Y. Wang et al. The sketch of the derivation is as follows: Consider the inner product \(s = \sum_i^n w_i x_i\) between the weights \(w\) and input \(x\), which gives the raw activation of a neuron before the non-linearity. Densely connected convolutional networks (2016), G. Huang et al. 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 value of \(p = 0.5\) is a reasonable default, but this can be tuned on validation data. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks (2016), S. Bell et al. Also, bib file for all top-100 papers are available. The projection therefore corresponds to a rotation of the data in X so that the new axes are the eigenvectors. Sparse initialization. the data mean) must only be computed on the training data, and then applied to the validation / test data. Usually it is also assumed that the space of structures is very large and not easily enumerable. Before we can begin to train the network we have to initialize its parameters. Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig. In practice: It is most common to use a single, global L2 regularization strength that is cross-validated. Intriguing properties of neural networks (2014), C. Szegedy et al. We can use this to reduce the dimensionality of the data by only using the top few eigenvectors, and discarding the dimensions along which the data has no variance. In particular, the diagonal of this matrix contains the variances. We can then compute the [3072 x 3072] covariance matrix and compute its SVD decomposition (which can be relatively expensive). The L1 regularization has the intriguing property that it leads the weight vectors to become sparse during optimization (i.e. For example, a Neural Network layer that has very small weights will during backpropagation compute very small gradients on its data (since this gradient is proportional to the value of the weights). in Dropout: A Simple Way to Prevent Neural Networks from Overfitting (pdf) that complements the other methods (L1, L2, maxnorm). 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. The loss function then maximizes this probability. its number of inputs). We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. A Knowledge-Grounded Neural Conversation Model (2017), Marjan Ghazvininejad et al. Finding function in form: Compositional character models for open vocabulary word representation (2015), W. Ling et al. Exploring models and data for image question answering (2015), M. Ren et al. CS231n, Convolutional Neural Networks for Visual Recognition, Stanford University, CS224d, Deep Learning for Natural Language Processing, Stanford University, Oxford Deep NLP 2017, Deep Learning for Natural Language Processing, University of Oxford, Deep Learning Summer School 2016, Montreal, Bay Area Deep Learning School 2016, Stanford. Reading text in the wild with convolutional neural networks (2016), M. Jaderberg et al. Inverted Dropout: Recommended implementation example. Instance-aware semantic segmentation via multi-task network cascades (2016), J. Dai et al. MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc. What makes for effective detection proposals? [Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017. The expression above can look scary but the gradient on \(f\) is in fact extremely simple and intuitive: \(\partial{L_i} / \partial{f_j} = \sigma(f_j) - y_{ij}\) (as you can double check yourself by taking the derivatives). Learning a Deep Convolutional Network for Image Super-Resolution (2014, C. Dong et al. Those most relevant to this paper include: Box-constrained L-BFGS can reliably find adversarial examples. As foreshadowing, Convolutional Neural Networks also take advantage of this theme with methods such as stochastic pooling, fractional pooling, and data augmentation. Long short-term memory (1997), S. Hochreiter and J. Schmidhuber. You signed in with another tab or window. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotations and hallucinated facts). The backward pass remains unchanged, but of course has to take into account the generated masks U1,U2. The structured loss refers to a case where the labels can be arbitrary structures such as graphs, trees, or other complex objects. This ensures that all neurons in the network initially have approximately the same output distribution and empirically improves the rate of convergence. Intriguing properties of neural networks. Feature Visualization by Optimization. Another form of regularization is to enforce an absolute upper bound on the magnitude of the weight vector for every neuron and use projected gradient descent to enforce the constraint. Typical values of \(c\) are on orders of 3 or 4. Both losses above assume that there is a single correct answer \(y_i\). I am an Assistant Professor of Computer Science and Engineering at University of California, Santa Cruz.My research interest lies at the intersection of computer vision and machine learning, with the goal of building human-level computer vision systems. In this process, the data is first centered as described above. dataset and methods for multilingual image question (2015), H. Gao et al. 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. The L2 norm squared would compute the loss for a single example of the form: The reason the L2 norm is squared in the objective is that the gradient becomes much simpler, without changing the optimal parameters since squaring is a monotonic operation. \(\partial{L_i} / \partial{f_j}\)) is easily derived to be either \(\delta_{ij}\) with the L2 norm, or \(sign(\delta_{ij})\). It can be implemented by penalizing the squared magnitude of all parameters directly in the objective. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al. In practice, the current recommendation is to use ReLU units and use the w = np.random.randn(n) * sqrt(2.0/n), as discussed in He et al.. Batch Normalization. If you’re certain that classification is not appropriate, use the L2 but be careful: For example, the L2 is more fragile and applying dropout in the network (especially in the layer right before the L2 loss) is not a great idea. Region-based convolutional networks for accurate object detection and segmentation (2016), R. Girshick et al. This could greatly diminish the “gradient signal” flowing backward through a network, and could become a concern for deep networks. An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al. A flurry of recent papers in theoretical deep learning tackles the common theme of analyzing neural networks in the infinite-width limit. Wasserstein GAN (2017), M. Arjovsky et al. Another way to address the uncalibrated variances problem is to set all weight matrices to zero, but to break symmetry every neuron is randomly connected (with weights sampled from a small gaussian as above) to a fixed number of neurons below it. Dermatologist-level classification of skin cancer with deep neural networks (2017), A. Esteva et al. the class scores in classification) and the ground truth label. Classification has the additional benefit that it can give you a distribution over the regression outputs, not just a single output with no indication of its confidence. This gives the initialization w = np.random.randn(n) / sqrt(n). This turns out to be a mistake, because if every neuron in the network computes the same output, then they will also all compute the same gradients during backpropagation and undergo the exact same parameter updates. Together, these choices define the new form of the score function, which we have extended from the simple linear mapping that we have seen in the Linear Classification section. Please make sure to read the contributing guide before you make a pull request. This gif depicts the training dynamics of a neural network. Chen and Manning. By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts. さらに、道路標識を誤認させるようなことも可能という研究もあります。自動運転を行っている際に停止の標識が誤認されたら・・・と考えるとちょっとぞっとする … (2016), J. Hosang et al. increasing 1e-5 to be a larger number). Le. Dropout falls into a more general category of methods that introduce stochastic behavior in the forward pass of the network. Pointer networks (2015), O. Vinyals et al. # Assume input data matrix X of size [N x D], # whiten the data: This is likely because there are very few bias terms compared to all the weights, so the classifier can “afford to” use the biases if it needs them to obtain a better data loss. Perceptual losses for real-time style transfer and super-resolution (2016), J. Johnson 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). Note that we’re adding 1e-5 (or a small constant) to prevent division by zero. However, it is very important to zero-center the data, and it is common to see normalization of every pixel as well. 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 \(2 \lambda w\). Calibrating the variances with 1/sqrt(n). Note that this is not generally the case: For example ReLU units will have a positive mean. Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. Pitfall: all zero initialization. Empirical evaluation of gated recurrent neural networks on sequence modeling (2014), J. Chung et al. When faced with a regression task, first consider if it is absolutely necessary. Predicting Lecture Video Complexity.Nick Su, … 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? End-to-end memory networks (2015), S. Sukbaatar et al. During testing, the noise is marginalized over analytically (as is the case with dropout when multiplying by \(p\)), or numerically (e.g. Very Deep Convolutional Networks for Natural Language Processing (2016), A. Conneau et al. Networks Neural Networks NeurIPS Nexus Ngram NIPS NLP On-device Learning open source operating systems Optical Character Recognition optimization osdi osdi10 patents Peer Review ph.d. fellowship PhD Fellowship PhotoScan Physics PiLab Pixel Policy Professional Development Proposals Public Data Explorer publication Publications Quantum AI A curated list of the most cited deep learning papers (2012-2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems (2016), M. Abadi et al. Understanding convolutional neural networks (2016), J. Koushik. This intriguing and rigorous set of experiments indicate a role of cerebellar plasticity in long-term memory of cued fear conditioning. Attribute classification. It can also be shown that performing this attenuation at test time can be related to the process of iterating over all the possible binary masks (and therefore all the exponentially many sub-networks) and computing their ensemble prediction. Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. Looking at only the j-th dimension of the i-th example and denoting the difference between the true and the predicted value by \(\delta_{ij}\), the gradient for this dimension (i.e. For certain applications, approximate versions are popular. A fast learning algorithm for deep belief nets (2006), G. Hinton et al. Here, we assume a dataset of examples and a single correct label (out of a fixed set) for each example. For example, a binary classifier for each category independently would take the form: where the sum is over all categories \(j\), and \(y_{ij}\) is either +1 or -1 depending on whether the i-th example is labeled with the j-th attribute, and the score vector \(f_j\) will be positive when the class is predicted to be present and negative otherwise. 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. Notice that this is not the case with Softmax, where the precise value of each score is less important: It only matters that their magnitudes are appropriate. In comparison, final weight vectors from L2 regularization are usually diffuse, small numbers. This gives the initialization w = np.random.randn(n) * sqrt(2.0/n), and is the current recommendation for use in practice in the specific case of neural networks with ReLU neurons. Deformable Convolutional Networks (2017), J. Dai et al. 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. As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their application domain. As a solution, it is common to initialize the weights of the neurons to small numbers and refer to doing so as symmetry breaking. We do not expand on this technique here because it is well described in the linked paper, but note that it has become a very common practice to use Batch Normalization in neural networks. Each label is then represented as a path along the tree, and a Softmax classifier is trained at every node of the tree to disambiguate between the left and right branch. The whitening operation takes the data in the eigenbasis and divides every dimension by the eigenvalue to normalize the scale. An example of other research in this direction includes DropConnect, where a random set of weights is instead set to zero during forward pass. Furthermore, the covariance matrix is symmetric and positive semi-definite. Warning: It’s not necessarily the case that smaller numbers will work strictly better. Therefore, we still want the weights to be very close to zero, but as we have argued above, not identically zero. The second common choice is the Softmax classifier that uses the cross-entropy loss: Problem: Large number of classes. words in English dictionary, or ImageNet which contains 22,000 categories), computing the full softmax probabilities becomes expensive. Gated Feedback Recurrent Neural Networks (2015), J. Chung et al. In the last step we assumed that all \(w_i, x_i\) are identically distributed. One weakness of this transformation is that it can greatly exaggerate the noise in the data, since it stretches all dimensions (including the irrelevant dimensions of tiny variance that are mostly noise) to be of equal size in the input. Deep generative image models using a laplacian pyramid of adversarial networks (2015), E.Denton et al. 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. Instead, have a strong preference to discretizing your outputs to bins and perform classification over them whenever possible. Please note that we prefer seminal deep learning papers that can be applied to various researches rather than application papers. We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Inverted dropout looks as follows: There has a been a large amount of research after the first introduction of dropout that tries to understand the source of its power in practice, and its relation to the other regularization techniques. It is not common to solve this problem as a simple unconstrained optimization problem with gradient descent. Exploring Neural Networks with Activation Atlases. Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015), C. Dyer et al. Addressing the rare word problem in neural machine translation (2014), M. Luong et al. Learning mid-level features for recognition (2010), Y. Boureau, A practical guide to training restricted boltzmann machines (2010), G. Hinton, Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio. Beyond short snippents: Deep networks for video classification (2015). In the previous section we introduced a model of a Neuron, which computes a dot product following a non-linearity, and Neural Networks that arrange neurons into layers. Deep Photo Style Transfer (2017), F. Luan et al. via sampling, by performing several forward passes with different random decisions and then averaging over them). For that reason, some papers that meet the criteria may not be accepted while others can be. It is also possible to perform dropout right on the input layer, in which case we would also create a binary mask for the input X. A typical number of neurons to connect to may be as small as 10. It is not very common to regularize different layers to different amounts (except perhaps the output layer). Accurate, Large Minibatch SGD:Training ImageNet in 1 Hour (2017), Priya Goyal et al. 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. Brain tumor segmentation with deep neural networks (2017), M. Havaei et al. & Trainor, P. A. Neural crest stem cells: discovery, properties and potential for therapy. Additionally, batch normalization can be interpreted as doing preprocessing at every layer of the network, but integrated into the network itself in a differentiable manner. Deep voice: Real-time neural text-to-speech (2017), S. Arik et al.. PixelNet: Representation of the pixels, by the pixels, and for the pixels (2017), A. Bansal et al. In particular, a Neural Network performs a sequence of linear mappings with interwoven non-linearities. Professor Forcing: A New Algorithm for Training Recurrent Networks (2016), A. Lamb et al. It is possible and common to initialize the biases to be zero, since the asymmetry breaking is provided by the small random numbers in the weights. To the extent possible under law, Terry T. Um has waived all copyright and related or neighboring rights to this work. Improved semantic representations from tree-structured long short-term memory networks (2015), K. Tai et al. Common pitfall. Some people report improvements when using this form of regularization. Regression is the task of predicting real-valued quantities, such as the price of houses or the length of something in an image. Instead, special solvers are usually devised so that the specific simplifying assumptions of the structure space can be taken advantage of. Artificial intelligence (AI) is the field devoted to building artificial animals (or at least artificial creatures that – in suitable contexts – appear to be animals) and, for many, artificial persons (or at least artificial creatures that – in suitable contexts – appear to be persons). The last transformation you may see in practice is whitening. Intriguing properties of neural networks (2014), C. Szegedy et al. Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al. It is very often the case that you can get very good performance by training linear classifiers or neural networks on the PCA-reduced datasets, obtaining savings in both space and time. On the Origin of Deep Learning (2017), H. Wang and Bhiksha Raj. the Weston Watkins formulation): As we briefly alluded to, some people report better performance with the squared hinge loss (i.e. Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs (2015), M. Ballesteros et al. 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. 本文分享一个“万物皆可盘”的NLP对抗训练实现，只需要四行代码即可调用。盘他。最近，微软的FreeLB-Roberta [1] 靠着 对抗训练 (Adversarial Training) 在GLUE榜上超越了Facebook原生的Roberta，追一科技也 … In this section we will discuss additional design choices regarding data preprocessing, weight initialization, and loss functions. In the implementation, applying this technique usually amounts to insert the BatchNorm layer immediately after fully connected layers (or convolutional layers, as we’ll soon see), and before non-linearities. Are you talking to a machine? (Please read the contributing guide for further instructions, though just letting me know the title of papers can also be a big contribution to us.). The core observation is that this is possible because normalization is a simple differentiable operation. It only makes sense to apply this preprocessing if you have a reason to believe that different input features have different scales (or units), but they should be of approximately equal importance to the learning algorithm. Additionally, the L2 loss is less robust because outliers can introduce huge gradients. Learning deep architectures for AI (2009), Y. Bengio. That is, \(L = \frac{1}{N} \sum_i L_i\) where \(N\) is the number of training data. That is, the gradient on the score will either be directly proportional to the difference in the error, or it will be fixed and only inherit the sign of the difference. Neural Network and Deep Learning (Book, Jan 2017), Michael Nielsen. In third step we assumed zero mean inputs and weights, so \(E[x_i] = E[w_i] = 0\). In this paper, the authors end up recommending an initialization of the form \( \text{Var}(w) = 2/(n_{in} + n_{out}) \) where \(n_{in}, n_{out}\) are the number of units in the previous layer and the next layer. To see this, consider an output of a neuron \(x\) (before dropout). On the importance of initialization and momentum in deep learning (2013), I. Sutskever et al. Mind's eye: A recurrent visual representation for image caption generation (2015), X. Chen and C. Zitnick. Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton, Deep learning in neural networks: An overview (2015), J. Schmidhuber. E.g. L1 regularization is another relatively common form of regularization, where for each weight \(w\) we add the term \(\lambda \mid w \mid\) to the objective. Deep learning (Book, 2016), Goodfellow et al. At test time, when we keep the neuron always active, we must adjust \(x \rightarrow px\) to keep the same expected output. One of its appealing properties is that network cannot “explode” even when the learning rates are set too high because the updates are always bounded. Structured prediction. Intuitively, it requires a very fragile and specific property from the network to output exactly one correct value for each input (and its augmentations). Crucially, note that in the predict function we are not dropping anymore, but we are performing a scaling of both hidden layer outputs by \(p\). Cell Res. 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. Linguistic Regularities in Continuous Space Word Representations (2013), T. Mikolov et al. Find out how by reading the rest of this post. An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. # divide by the eigenvalues (which are square roots of the singular values), """ Vanilla Dropout: Not recommended implementation (see notes below) """, # probability of keeping a unit active. higher = less dropout, # forward pass for example 3-layer neural network, # backward pass: compute gradients... (not shown), # perform parameter update... (not shown), """ Notice that loss is accumulated if a positive example has score less than +1, or when a negative example has score greater than -1. A recently developed technique by Ioffe and Szegedy called Batch Normalization alleviates a lot of headaches with properly initializing neural networks by explicitly forcing the activations throughout a network to take on a unit gaussian distribution at the beginning of the training.

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