gives a quick insight into some of the research into distillation related methods. Distillation in Scientific Literature A number of researchers have demonstrated the effectiveness of the distillation technique

them. Concretely, a practitioner might want to experiment with at least the following aspects: 1. Computing saliency scores. 2. Deciding on a pruning schedule. 3. Unstructured / Structured sparsity. Seems derivative gives us a clearer insight into how important might be to minimize the loss. Since computing pairwise second-derivatives for all and might be very expensive (even with just weights, this proportion to the mean magnitude of momentum of weights in that layer. There might be other ways of computing saliency scores, but they will all try to approximate the importance of a given weight at a certain

observed that, the feature mapping leads to a huge number number of new features, such that i) computing the mapping itself can be inefficient, especially when the new feature space is of much higher dimension; can be expensive (e.g., we have to store all the high-dimensional images of the data samples and computing inner products in the high-dimensional feature space is of considerable overhead). Fortunately, implicitly defines a mapping φ(x) = {x2 1, √ 2x1x2, x2 2} Through the kernel function, when computing the inner product < φ(x), φ(z) >, we do not have to map x and z into the new higher-dimensional

which combined the accuracy and latency metrics. It searched for Pareto optimal child networks by computing their latencies 7 Tan, Mingxing, et al. "Mnasnet: Platform-aware neural architecture search for defines a ChildManager class which is responsible for spawning child networks, training them, and computing rewards. The layers constant defined in the class indicates the stacking order of the cells. Each the second step, the child network is training on the CIFAR-10 dataset. The third step involves computing reward which is the difference between the accuracy and the rolling average of past accuracies over

approaches towards efficiency is compression to reduce data size. For the longest time in the history of computing, scientists have worked tirelessly towards storing and transmitting information in as few bits as Learning model comes from the weights in its layers. Similarly, most of the latency comes from computing the activations. Typically, the weights and activations are 32-bit floating-point values. One of value xmax to 2b-1 (b is the number of bits of precision and b < 32). Notice how this is similar to computing the xmin and xmax of an arbitrary matrix. 2. Then we can map all the values in the weight matrix

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Overcoming catastrophic forgetting in neural networks Memory-Augmented Neural Networks Source: Hybrid computing using a neural network with dynamic external memory Work Fun Play Hard

centroids and replace each pixel in the image with its nearest cluster centroid color. Because computing cluster centroids on a 538x538 image would be time- consuming on a desktop computer, you will instead

17 / 19 Backpropagation Algorithm • The backpropagation equations provides us with a way of computing the gradient of the cost function • Input: Set the corresponding activation a[1] for the input

loss loss y_train_tensor Misc • Dynamic VS Static Computation Graph Building the graph and computing the graph happen at the same time. Seems inefficient, especially if we are building the same