This paper presents a simple computational procedure for generating 'matching' or 'cloning' datasets so that they have exactly the same fitted multiple linear regression equation. The method is simple to implement and provides an alternative to generating datasets under an assumed model. The advantage...
Lab-3: Using L2-level API to implement a single-kernel acceleration for JPEG decoding Lab purpose Operation steps (1) Understand the Work Directory (2) Build kernel for different modes (3) Run kernel in Software-Emulation mode (4) Run kernel in Hardware-Emulation mode (5) Run ...
Lab-3: Using L2-level API to implement a single-kernel acceleration for JPEG decoding Lab purpose Operation steps (1) Understand the Work Directory (2) Build kernel for different modes (3) Run kernel in Software-Emulation mode (4) Run kernel in Hardware-Emulation mode (5) Run ...
A basic CNN network of two CNN layers, a dense layer and an output layer to recognize the picture of cat or dog. Tensorflow and Keras have been used to implement the layers. The dataset used here is the Kaggle dataset provided by Microsoft. classifier deep-learning jupyter-notebook cnn ...
Perspective transform.Since scanned pages can sometimes look stretched, we implement Perspective TransformFootnote13in which a part of the image (formed by randomly selecting 4 points) is stretched. Random affine rotation and perspective transform might cause geometric changes in the images which could ...
but are only informative over a certain range. This leads us to the idea that particular samples may be classifiable or unclassifiable according to any given disease biomarker. Here, we present and implement a method of biomarker discovery that relies upon restricting training datasets to classifi...
We go beyond just masking weights and implement a structured pruning approach reducing the model architecture to layer sizes as low as [1, 1] while improving accuracy in comparison to large model sizes like [1600, 800] for the alcohol dataset. Finally, for each dataset, we show the ...
KNNs is easy to implement and adapts well to the structure of the data but is sensitive to the choice of ’k’ and irrelevant features. It can also be slow for large datasets, as it calculates distances to all points during prediction [59]. Logistic Regression predicts the probability of ...
the non-temporal data is fed into FFN and temporal data is input to GRU and then the outputs of the FFN and GRU are combined in a shared latent representation layer. 4.3. Implementation details We implement the Super Learner algorithm using R packages and Python libraries listed in Table 7....
In intrusion detection, t-SNE and PCA are often used to implement network traffic visualization. Compared with other preprocessing methods, feature selection and feature extraction are the research points of many articles. Among all the papers we investigated, 38 focus on making improvements to ...