[CVPR 2024 Award Candidate] Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

[ECCV 2024] Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention

map estimation of topic models

DRAM error-correction code (ECC) simulator incorporating statistical error properties and DRAM design characteristics for inferring pre-correction error characteristics using only the post-correction errors. Described in the 2019 DSN paper by Patel et al.: https://people.inf.ethz.ch/omutlu/pub/understanding-and-modeling-in-DRAM-ECC_dsn19.pdf.

Implementation of framework and reproduction of figures from "A Modularized Efficient Framework for Non-Markov Time Series Estimation" (https://arxiv.org/abs/1706.04685)

Assignments completed for my Machine Learning course: Topics include probability and statistics proofs, MLE/MAP parameter estimation, EM Algorithm, Bayes Theorem implementations, gradient descent methods, Neural Networks and Deep Learning.

Assignment 5, Data Analysis and Interpretation, Autumn 2020, IIT Bombay

Binary classification project in Python comparing a MAP classifier (with known distributions) vs. logistic regression. Includes analytical posterior derivation, Monte Carlo simulation, and gradient-based training. Focus on error probability, variance impact, and model performance.

This is a repository with the assignments of IE675b Machine Learning course at University of Mannheim.