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particle-filter-1

Particle filters or Sequential Monte Carlo (SMC) methods are a set of genetic, Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made, and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of some Markov process, given some noisy and partial observations. Particle filters implement the prediction-updating transitions of the filtering equation directly by using a genetic type mutation-selection particle algorithm. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function.

particle-filter-prototype

Particle Filter Implementations in Python and C++, with lecture notes and visualizations

particle-swarm-optimization

Learn about particle swarm optimization (PSO) through Python!

particle-track-reconstruction

Using Machine Learning to reconstruct trajectories of particles detected as hits by ATLAS experiment at LHC, Cern.

particle-trajectory-reconstruction-delaunay

particleswarmoptimization

Matlab implementation of particle swarm optimization. Well documented with examples.

pathlosspredictionsatelliteimages

Prediction of path loss using satellite images and convoluted neural networks

patterns-at-gastech

VAST Challenge 2014: Mini-Challenge 2

pedestrian_animation

Python code for animating Pedestrian-DataSet(such as UCY and ETH)

peer-review-bidding

Tanner Fiez, Nihar Shah, Lillian Ratliff. "A SUPER* Algorithm to Optimize Paper Bidding in Peer Review" ICML Workshop on Real-world sequential decision making: reinforcement learning and beyond.

penminer

Measuring the persistence of activity snippets in evolving networks (KDD '20)

performing-exploratory-data-analysis-on-pubg-dataset

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 players per group.

personalizedmultitasklearning

Code for performing 3 multitask machine learning methods: deep neural networks, Multitask Multi-kernel Learning (MTMKL), and a hierarchical Bayesian model (HBLR).

pfrnns

Particle Filter Recurrent Neural Networks (AAAI 2020)

phd

My PhD Papers and Presentations

phd-defense

Slides (with sources) for defending the doctoral dissertation "Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams"

phd-presentations

Repo for all of my public Ph.D. presentations.

phd-thesis

My Ph.D. thesis on Outlier Selection and One-Class Classification

phd-thesis-1

Ph.D. Thesis - Scalable Human Identification with Deep Learning

phd-thesis-2

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

phd-thesis-3

Repository of my thesis "Understanding Random Forests"

phd-thesis-4

Big-data analytics using rules-based techniques and machine learning to support business decisions

phd-thesis-sample

Partial LaTeX sources of my PhD thesis

phd-thesis-talk

Presentation of PhD thesis

phdpresentation

phdthesis

Tools and contribution associated with the PhD thesis tiled Optimization of Future Mobile Communication Systems using Deep Learning

phdthesis-1

Ph.D. Thesis from Goldsmiths, University of London entitled, "Audiovisual Scene Synthesis". Hosts all images and latex files.

phdthesis-2

LaTeX source for my PhD thesis "Finding patterns in complex biomedical data using networks and molecular profiling"

phdthesis_deprecated

phil

PHIL - Position Hallucination and Indoor Localization