Essays about: "Tabular data"

Showing result 1 - 5 of 45 essays containing the words Tabular data.

1. 1. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Baptiste Bremond; [2024]
   Keywords : TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

   Abstract : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. READ MORE

3. 2. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder

   University essay from Lunds universitet/Fysiska institutionen

   Author : Max Svensson; [2024]
   Keywords : Machine Learning; Self-supervised learning; AI; Physics; Medicine; Physics and Astronomy;

   Abstract : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. READ MORE

4. 3. Leveraging CNN for Automated Peak Picking in Untargeted Metabolomics without Parameter Dependencies

   University essay from Göteborgs universitet/Institutionen för data- och informationsteknik

   Author : Vivian Wang; Lidia Yalew; [2023-10-19]
   Keywords : Deep learning; Convolutional neural network; LC-MS; Peak Detection; Metabolomics; Faster R-CNN;

   Abstract : Metabolomics is a scientific discipline that involves the thorough analysis of small molecules, known as metabolites, found within a biological system. Furthermore, liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in metabolomics for analysing biological samples due to its broad coverage of the measurable metabolome. READ MORE

5. 4. Combining Cell Painting, Gene Expression and Structure-Activity Data for Mechanism of Action Prediction

   University essay from Uppsala universitet/Nationellt resurscentrum för biologi och bioteknik

   Author : Erik Everett Palm; [2023]
   Keywords : bioinformatics; deep learning; machine learning; joint model; tabular data; image data;

   Abstract : The rapid progress in high-throughput omics methods and high-resolution morphological profiling, coupled with the significant advances in machine learning (ML) and deep learning (DL), has opened new avenues for tackling the notoriously difficult problem of predicting the Mechanism of Action (MoA) for a drug of clinical interest. Understanding a drug's MoA can enrich our knowledge of its biological activity, shed light on potential side effects, and serve as a predictor of clinical success. READ MORE

6. 5. Exploring State-of-the-Art Machine Learning Methods for Quantifying Exercise-induced Muscle Fatigue

   University essay from Högskolan i Halmstad/Akademin för informationsteknologi

   Author : Abboud Afram; Danial Sarab Fard Sabet; [2023]
   Keywords : EMG; SEMG; STFT; CWT; SVM; CNN; GAN; DCGAN; BCE; SGD; deep learning; machine learning; muscle fatigue; DCGAN; spectrogram; CNN models; transfers learning; data augmentation; feature extraction;

   Abstract : Muscle fatigue is a severe problem for elite athletes, and this is due to the long resting times, which can vary. Various mechanisms can cause muscle fatigue which signifies that the specific muscle has reached its maximum force and cannot continue the task. READ MORE