Greedy optimization algorithm. حسب Alexis Bénichou (19832361)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) حسب Daniel Pérez Palau (11097348)

"…</p><h2>Model Architecture</h2><p dir="ltr">The model is based on <code>pysentimiento/robertuito-base-uncased</code> with the following modifications:</p><ul><li>A dense classification layer was added over the base model</li><li>Uses input IDs and attention masks as inputs</li><li>Generates a multi-class classification with 5 hate categories</li></ul><h2>Dataset</h2><p dir="ltr"><b>HATEMEDIA Dataset</b>: Custom hate speech dataset with categorization by type:</p><ul><li><b>Labels</b>: 5 hate type categories (0-4)</li><li><b>Preprocessing</b>:</li><li>Null values ​​removed from text and labels</li><li>Reindexing and relabeling (original labels are adjusted by subtracting 1)</li><li>Exclusion of category 2 during training</li><li>Conversion of category 5 to category 2</li></ul><h2>Training Process</h2><h3>Configuration</h3><ul><li><b>Batch size</b>: 128</li><li><b>Epoches</b>: 5</li><li><b>Learning rate</b>: 2e-5 with 10% warmup steps</li><li><b>Early stopping</b> with patience=2</li><li><b>Class weights</b>: Balanced to handle class imbalance</li></ul><h3>Custom Metrics</h3><ul><li>Recall for specific classes (focus on class 2)</li><li>Precision for specific classes (focus on class 3)</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.6 (class 3)</li><li>Precision at recall=0.6 (class 2)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Per-class metrics</li><li>Confusion matrix</li><li>Full classification report</li></ul><h2>Technical Features</h2><h3>Data Preprocessing</h3><ul><li><b>Tokenization</b>: Maximum length of 128 tokens (truncation and padding)</li><li><b>Encoding of labels</b>: One-hot encoding for multi-class classification</li><li><b>Data split</b>: 80% training, 10% validation, 10% testing</li></ul><h3>Optimization</h3><ul><li><b>Optimizer</b>: Adam with linear warmup scheduling</li><li><b>Loss function</b>: Categorical Crossentropy (from_logits=True)</li><li><b>Imbalance handling</b>: Class weights computed automatically</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required:</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li><li>numpy</li></ul><h2>Usage</h2><ol><li><b>Data format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Required column name: <code>text</code> (string type)</li><li>Required column name: Data type label (integer type, 0-4) - optional for evaluation</li></ul><ol><li><b>Text preprocessing</b>:</li></ol><ul><li>Automatic tokenization with a maximum length of 128 tokens</li><li>Long texts will be automatically truncated</li><li>Handling of special characters, URLs, and emojis included</li></ul><ol><li><b>Label encoding</b>:</li></ol><ul><li>The model classifies hate speech into 5 categories (0-4)</li><li><code>0</code>: Political hatred: Expressions directed against individuals or groups based on political orientation.…"

Python implementation from Symplectic decomposition from submatrix determinants حسب Jason L. Pereira (11598632)

"…Python implementation of the algorithm and demonstration of how to use the functions.…"

Illustration of the unrestricted model and its training algorithm. حسب Yuru Song (21300760)

Limitation of the conventional DBT reconstruction algorithm. حسب Yunsu Choi (11993484)

Fitting results of the different optimization algorithms applied. حسب Sonja Langthaler (6069149)

Functional annotations of peaks. حسب Azusa Tanaka (540528)

WGCNA and machine learning algorithms. حسب Shushu Wang (6870191)

Performance of optimization algorithms in six different network types. حسب Maurits H. W. Oostenbroek (11331123)

GraSPy: an Open Source Python Package for Statistical Connectomics حسب Benjamin Pedigo (6580352)

"…We developed GraSPy, an open-source Python toolkit for statistical inference on graphs. …"

Example performance of the smooth-index algorithm. حسب Alexander Borst (32619)

"…<b>D</b> Different cost functions as the algorithm performs the gradient descent. …"

Face matching performance as a function of survey condition for Study 2 (threshold = 0.5). حسب Daniela Barragan (13146762)

Face matching performance as a function of test condition for Study 1 (threshold = 0.5). حسب Daniela Barragan (13146762)

Modular architecture design of PyNoetic showing all its constituent functions. حسب Gursimran Singh (575288)

الموضوعات:

Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. حسب Gursimran Singh (575288)

الموضوعات:

Description: Subnetwork of TFRC from functional tissue network of PDAC. حسب Enze Liu (6286262)

The number of inactive nodes as a function of time steps in (a) a random network and (b) a scale-free network. حسب Waseem Al-Aqqad (14123917)

One iteration of the NSGA-II algorithm. حسب Erick Axel Padilla-García (16839233)

The workflow used to analyze imbalanced and balanced sequences. حسب Solomon Shiferaw Beyene (9143178)

Application of the smooth-index to bandwidth-limited matrices (A,B) and to block-diagonal matrices (C,D). حسب Alexander Borst (32619)