As we discussed in the Linear Classification section, due to multiplicative interactions between weights and inputs this has the appealing property of encouraging the network to use all of its inputs a little rather than some of its inputs a lot. We will go into details of these methods later. 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. DeepPose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy. 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. """, CS231n Convolutional Neural Networks for Visual Recognition, Understanding the difficulty of training deep feedforward neural networks, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Dropout Training as Adaptive Regularization, The recommended preprocessing is to center the data to have mean of zero, and normalize its scale to [-1, 1] along each feature. Visualizing and Understanding Recurrent Networks (2015), A. Karpathy et al. (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.). It's generative because unlike other neural networks that spit out a numeric score or a yes or no answer, GPT-3 can generate long sequences of original text as its output. 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). E.g. Recurrent neural network regularization (2014), W. Zaremba et al. We can examine the variance of \(s\): where in the first 2 steps we have used properties of variance. 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. 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. 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. via sampling, by performing several forward passes with different random decisions and then averaging over them). Unsupervised learning of video representations using LSTMs (2015), N. Srivastava et al. Structured prediction. 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. Fast and accurate deep network learning by exponential linear units (ELUS) (2015), D. Clevert et al. Also, bib file for all top-100 papers are available. Another form of this preprocessing normalizes each dimension so that the min and max along the dimension is -1 and 1 respectively. 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). 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. Find out how by reading the rest of this post. Thank you for all your contributions. (2014b) demonstrated a variety of intriguing properties of neural networks and related models. 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. Efficient piecewise training of deep structured models for semantic segmentation (2016), G. Lin 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 \(1/n\). the Weston Watkins formulation): As we briefly alluded to, some people report better performance with the squared hinge loss (i.e. A typical number of neurons to connect to may be as small as 10. Dean et al. Greedy layer-wise training of deep networks (2007), Y. Bengio et al. Neural Machine Translation and Sequence-to-sequence Models(2017): A Tutorial, G. Neubig. A thorough examination of the cnn/daily mail reading comprehension task (2016), D. Chen et al. Intriguing properties of neural networks (2014), C. Szegedy et al. The last transformation you may see in practice is whitening. Reducing the dimensionality of data with neural networks, G. Hinton and R. Salakhutdinov. If we were to compute the covariance matrix of Xrot, we would see that it is now diagonal. As a solution, it is common to initialize the weights of the neurons to small numbers and refer to doing so as symmetry breaking. Improving distributional similarity with lessons learned from word embeddings, O. Pitfall: all zero initialization. Hence, an example is classified as a positive example (y = 1) if \(\sigma (w^Tx + b) > 0.5\), or equivalently if the score \(w^Tx +b > 0\). # 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. At test time, when we keep the neuron always active, we must adjust \(x \rightarrow px\) to keep the same expected output. PCA and Whitening is another form of preprocessing. An Empirical Exploration of Recurrent Network Architectures (2015), R. Jozefowicz et al. The geometric interpretation of this transformation is that if the input data is a multivariable gaussian, then the whitened data will be a gaussian with zero mean and identity covariance matrix. 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. Recurrent neural network based language model (2010), T. Mikolov et al. A more recent paper on this topic, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification by He et al., derives an initialization specifically for ReLU neurons, reaching the conclusion that the variance of neurons in the network should be \(2.0/n\). 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? 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). A nice property of np.linalg.svd is that in its returned value U, the eigenvector columns are sorted by their eigenvalues. Understanding neural networks through deep visualization (2015), J. Yosinski et al. Weakly supervised object localization with multi-fold multiple instance learning (2017), R. Gokberk et al. Describing videos by exploiting temporal structure (2015), L. Yao et al. Max norm constraints. 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. Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all. 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. 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. 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. It depends on the impact of the paper, applicability to other researches scarcity of the research domain, and so on.