Personal Project, Ebot7 GmbH, Munich, Germany
Undertook a solo research project to implement a multilingual sentence similarity model (de,en,fr). This model performs 3% points better and has 74% smaller embeddings than the current best ebot7 model.
Company Project, Ebot7 GmbH, Munich, Germany
A Comparison and benchmarking of different Approximate Nearest Neighbor(ANN) Libraries.
Master Thesis, Technical University of Munich, Munich, Germany
A novel approach to jointly detect aspect and the corresponding sentiment in a given sentence using DNN and transfer learning.
Seminar Presentation, Technical University of Munich, Munich, Germany
The goal of this seminar was to explore the classical three stage architecture along with it’s several tasks in detail. A data driven news generation system for automated journalism was also explored.
Interdisciplinary Project, Technical University of Munich, Munich, Germany
In this project we are trying to develop a system that will augment the volumetric medical images onto a segmented body part. In order to correctly augment medical information onto the patient it is essential to accurately determine the transformation between the current pose of the patient, the viewing point of the observer, and the associated medical image data. The required transformations can be estimated by registering the surface (skin) of the patient with a surface extracted from the associated volumetric scans.
Practical Course Project, Technical University of Munich, Munich, Germany
In this project we implemented a web interface for visualizing the working of a protein structure predicting Deep Neural Network using guided backpropagation technique. The goal of this technique is to visualize parts of the input that most activates a given neuron. An effective way of doing this is to visualize gradients of activations(output) with respect to the input.
Course Project, Technical University of Munich, Munich, Germany
This project evaluates several algorithms for iterative closest point algorithm. Iterative closest Point (ICP) is an algorithm employed to minimize the difference between two point clouds given an initial estimate of the relative pose. It is often used to reconstruct 2D or 3D surfaces from different scans, to localize robots and achieve optimal path planning and to register medical scans. ICP has several steps and each step may be implemented in various ways which give rise to a multitude of ICP variants. In our project, we implement and analyze several variants of ICP, comparing them on the basis of execution speed and quality of the result.
Interdisciplinary Project, Technical University of Munich, Munich, Germany
This project helps you create random human poses with the help of Makehuman and then generates their rgb and depth data using Blender. The main motivation for this project was scarcity of existing datasets for human body segmentation from depth images. The data generation pipeline is capable of generating different body types and poses and thus creating a varied and large database of depth and segmented images.
Course Project, Technical University of Munich, Munich, Germany
This work implements the architecture laid out in the paper by Li, Jun, Reinhard Klein, and Angela Yao. “Learning fine-scaled depth maps from single RGB images.” arXiv preprint (2016).
Practical Course Project, Technical University of Munich, Munich, Germany
Through this work, we propose the incorporation of Inverse Autoregressive Flows for determining the state space (latents) in a dynamical system model. This reduces the number of samples that need to be obtained in order to approximate the posterior distribution (and thus the underlying states/latents for a set of observations and controls) from one per time step to one per sequence of observations. Our experiments with pendulum-v01, an environment from openai gym confirmed that the accuracy with which the observations are generated are close to the state of the art for sequence models.
Practical Course Project, Technical University of Munich, Munich, Germany
InfoGAN is almost similar to GAN except for the fact that it is also able to learn disentangled(interpretable) representations in a completely unsupervised manner. These interpretable features can be used by many downstream tasks such as classification, regression, visualization, and policy learning in reinforcement learning. The code in this repository was reimplemented on top the official repository of the paper - InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
Final Btech Project, Delhi Technological University, New Delhi, India
It is an automatic summarization tool that generates summary of a given text using linguistic, statistical and extractive methods. The algorithm for generating summary was implemened in C++ and the user interface was implemented in Visual Basic. It was a group project submitted by a group of 4 studennts in partial fulfillment of the requirement for the award of the degree of Bachelor of Technology in Software Engineering, to the Delhi Technological University in 2013.
Minor Project, Delhi Technological University, New Delhi, India
This project tries to classify an incoming email as spam or ham using a Naive Bayes Classifier. The algorithm that we have used gives a strong baseline for the task. Infact many email clients use some variation of this algorithm for detecting spam emails.