73728 Block: conv_transpose_block_17 Sparsity: 0.0% Total Weights: 1728 Figure 5-5 shows the comparison of compressed sizes of our regular model and its 50% sparse version. We used Tensorflow's save_model() even higher sparsity values and observe the resulting accuracies. Figure 5-5: Compressed size comparison of a regular model with its 50% sparse version. In this project, we used a consistent sparsity codebook will cost us bytes to store. For each tensor element we will now store only the index of its centroid in the codebook, which will only take up bits. For a tensor with elements, the cost would be

by the phases of architectural breakthroughs to improve on previous results and to drive down the cost of achieving those results. The evolution of multilayer perceptrons was one of the biggest architectural of the words would get mapped to the OOV token. However, if is too large, we would have to pay the cost of a very large embedding table. Step 2: Dataset Preparation & Vectorization Once the window size occupy a significant portion of the model size on disk and in memory. Although this comes with the cost of the table taking up significant disk space and memory, this issue can be a bottleneck if the model

Similarly, if you could make the model training process label efficient, you would incur a lower cost to meet a performance benchmark. Refer to Figure 3-3 for an example of a label efficient model’s training illustrates how learning techniques are leveraged to reduce the model footprint. Table 3-2 shows a comparison of vanilla models (without the learning techniques) with the models that employ learning techniques students. The creators went through a tedious sample collection and digitization process. It would cost substantial labor, time and money to collect more samples. In 2019, Kaggle1 opened a competition to

(important parameters) than those in the rest of the states (unimportant parameters). Figure 7-2: A comparison of hyperparameter search algorithms for two hyperparameters. The blue contours show the regions hyperparameter gets a new value per trial, the unimportant parameters do not increase the evaluation cost. Figure 7-2 (b) shows an example of random search. The trials are randomly spread across the search represented as a conditional probability distribution . The surrogates are cheaper to compute in comparison to the blackbox model (the deep learning model). Hence, they can be executed much more frequently

same target dataset, the authors report needing fewer labeled examples. Refer to figure 6-6 for a comparison between the error obtained by training from scratch v/s using pre-training strategies. 5 WikiText-103 However, since the pre-trained model is intended to be generalizable across many downstream tasks, the cost of pre-training can be amortized amongst these tasks. BERT has been used across a large number of achieves an accuracy of 91.59% while the latter achieves 90.33%. Refer to figure 6-8. Figure 6-8: Comparison between the accuracies achieved by BERT-Small models when using and not-using the pre-trained

θ2x2 + θ3x3 + θ4x4 To eliminate the influence of θ3x3 and θ4x4 to smoothen hypothesis function, the cost function can be modified as follows min θ 1 2m � m � i=1 (hθ(x(i)) − y(i))2 + 1000 · θ2 3 + 1000 As the magnitudes of the fitting parameters increase, there will be an increasing penalty on the cost function This penalty is dependent on the squares of the parameters as well as the magnitude of λ Regularization and Bayesian Statistics September 20, 2023 9 / 25 Regularized Logistic Regression Recall the cost function for logistic regression J(θ) = − 1 m m � i=1 [y(i) log(hθ(x(i))) + (1 − y(i)) log(1 −

number of underexposed images that cover limited lighting conditions. Our Dataset Quantitative Comparison: Our Dataset Method PSNR SSIM HDRNet 26.33 0.743 DPE 23.58 0.737 White-Box 21.69 0.718 Distort-and-Recover 0.893 Quantitative Comparison: MIT-Adobe FiveK Visual Comparison: Our Dataset Input JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched Visual Comparison: MIT-Adobe FiveK Input Input JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Comparison Results: User Study Input WVM JieP HDRNet DPE White-Box Distort-and-Recover Our result Limitaion Input

print(x_q) This returns the following result. [0 1 2 3 4 5 6 7 7] Table 2-2 shows the element wise comparison of x and xq. x -10.0 -7.5 -5.0 -2.5 0 2.5 5.0 7.5 10.0 xq 0 1 2 3 4 5 6 7 7 Table 2-2: Vector ,3]) weights = get_random_matrix([3,5]) bias = get_random_matrix([5]) Print the weights for comparison later on. print(weights) [[-0.08321415 -0.66657766 0.71264132 -0.39179407 0.05601718] [-0.85867389 function, which can be implemented by invoking the np.maximum on y, such that it does element-wise comparison between each element and 0, and returns the larger value. Recall that the ReLU(x) = x if x > 0

Iterative training Semi-supervised Labeling Incremental training Data Augment Model comparison Model Fusion Gray Update Auto Evaluation Log Server Graph Abstraction Data Flow API

use API. The code execution in this framework is quite easy. Also need a fewer lines to code in comparison. • It is easy to debug and understand the code. • Python usage − This library is considered to