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Manipulating state space distributions for sample-efficient imitation-learning
(Georgia Institute of Technology, 2020-03-16)
Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents ...
Large scale machine learning for geospatial problems in computational sustainability
(Georgia Institute of Technology, 2020-05-14)
The UN laid out 17 Sustainable Development Goals as part of the “The 2030 Agenda for Sustainable Development”. Each goal consists of broad targets - such as increasing the percentage of forested land (indicator 15.1.1) - ...
Deep representation learning on hypersphere
(Georgia Institute of Technology, 2020-07-27)
How to efficiently learn discriminative deep features is arguably one of the core problems in deep learning, since it can benefit a lot of downstream tasks such as visual recognition, object detection, semantic segmentation, ...
Deep-learning for automated diatom detection and identification for the ecological diagnosis of fresh-water environments
(Georgia Institute of Technology, 2020-07-29)
Diatoms are a type of unicellular microalgae found in all aquatic environments. Their great diversity and ubiquity make these organisms recognized bio-indicators for monitoring the ecological status of watercourses, notably ...
Building agents that can see, talk, and act
(Georgia Institute of Technology, 2020-04-25)
A long-term goal in AI is to build general-purpose intelligent agents that simultaneously possess the ability to perceive the rich visual environment around us (through vision, audition, or other sensors), reason and infer ...
Encoding 3D contextual information for dynamic scene understanding
(Georgia Institute of Technology, 2020-04-27)
This thesis aims to demonstrate how using 3D cues improves semantic labeling and object classification. Specifically, we will consider depth, surface normals, object classification, and pixel-wise semantic labeling in this ...
Domain adaptation via data augmentation
(Georgia Institute of Technology, 2020-04-28)
Deep learning (DL) models require large labeled datasets for training. Practitioners often need to adapt an existing DL model to a different domain. For instance, a practitioner in a company developing autonomous vehicles ...
Interactive Scalable Interfaces for Machine Learning Interpretability
(Georgia Institute of Technology, 2020-12-01)
Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has ...
Prokaryotic Gene Start Prediction: Algorithms for Genomes and Metagenomes
(Georgia Institute of Technology, 2020-12-01)
Prokaryotic gene-prediction is the task of finding genes in archaeal or bacterial DNA sequences. These genomes consist of alternating gene-coding and non-coding regions, meaning the task is solved by determining the start ...