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2018-ma-notebooks

2019

Tehnologii GNOME.

2019-kdd-automl

2019kdd_automl

2020_kdd_debiasing_top13

KDD Cup 2020 Challenges for Modern E-Commerce Platform: Debiasing Full榜15 Half榜13

2020_summer_internship

2021centraldataopen

Code from the 2021 Citadel Central Region Data Open competition.

2change

Change detection (for jl)

3d-hand-pose-sequence-data-augmentation-using-gans

The human hand plays a crucial role in conveying emotions and carrying out most day-to-day activities. Therefore numerous modern technologies - ranging from gesture control to autonomous driving - would benefit from the reliable recognition of certain hand actions. This can be done using a two-step approach, in which first hand poses are obtained from video frames and then the resulting sequences are classified in the 3D skeleton space. Existing techniques that aim to solve the second step are mostly based on deep learning methods. Given the high complexity and dimensionality of the human hand, these require large amounts of training data to achieve good performance. As the collection of precisely annotated hand pose data is time-consuming and expensive, data augmentation appears as an advantageous practice to increase the recognition accuracy for a given classifier. This thesis proposes a suitable WGAN-GP architecture for the generation of synthetic hand skeleton sequences with variable length. The recommended critic consists of a multi-layer perceptron with three hidden layers, while the generator is based on two RNNs and receives a start frame as input. Both networks are conditioned on the action class. The best performing model was trained on multiple classes simultaneously and selected based on the smallest generator loss. When its synthetic samples were used to augment the training set of a 1-layer LSTM classifier, the classification error on several subsets as well as on the complete dataset decreased. Quantitative results show that the chosen GAN-based data augmentation outperforms alternative standard methods. Furthermore, no clear correlation between the visual appearance of the generated samples and their resulting improvement on recognition accuracy was found.

3kazu8

3w_dataset

The first realistic and public dataset with rare undesirable real events in oil wells.

420aa-datamining

[420AA] Data Mining Part 1 and 2 - Clustering, time series, sequential patterns, classification, outlier detection @unipi A.Y. 2017-2018

4dsurvival

Deep learning cardiac motion analysis for survival prediction

5539-fall-2015-project

6010u-final

Python code for Kaggle Contest m5-forecasting

a-dgsclab

a-fuzzy-rough-set-based-feature-selection

a-real-time-application-to-detect-phishing-websites

Detection of Phishing Websites using Random Forest based on a Dataset available in UCI: ML Repository.

a-simple-search-engine-in-python

a-two-stage-stochastic-programming-supply-chain-network-design

A stochastic programming approach to modeling a supply chain network design.

a2c-te-nog

Repository for "Solving the scalarization issues of Advantage-based Reinforcement Learning Algorithms"

aa228-decisionmakingunderuncertainty

Fall 2016 - class @ Stanford. Structure Learning, Markov Decision Process, Reinforcement Learning // in progress

aaai-2015-demographics

Code and data for the AAAI 2015 paper entitled: "Predicting the demographics of Twitter users from social evidence using website traffic data"

aals-for-cp

Accelerated Alternating Least Squares algorithm for canonical tensor decomposition with convergence guarantees

aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

aas-and-ebmp

AUTOMATED ATTENDANCE SYSTEM BY FACE DETECTION AND EMOTION BASED MUSIC PLAYING AND WALLPAPER CHANGING SYSTEM

ab-test-peeking

abalone-model

MLOps with Amazon SageMaker and GitHub Actions

abdg

Attribute-based Decision Graph

abettor

An R package for connecting to the online betting exchange Betfair, via their API-NG product, using JSON-RPC.