The empirical successes of neural networks currently lack rigorous theoretical explanation. A first step might be to show that data generated by neural networks with a single hidden layer, smooth activation functions and ...

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 ...

Mosquitoes are directly responsible for the death of more than a million people each year. Yet the ability to mitigate their deadly impact or even monitor them in the wild to better understand their behavior remains ...

In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, we focus on two ...

Computer software is constantly increasing in complexity; this requires more developer time, effort, and knowledge in order to correct bugs inevitably occurring in software production. Eventually, increases in complexity ...

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, ...

Branching heuristics determine the performance of search-based SAT solvers. We note that recently, Neural Machine Learning approaches have been proposed to learn such heuristics from data. The first step in learning a ...

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 ...