This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks.
Significance: This proof of concept study demonstrates the technical feasibility of a smartwatch device and supervised machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols.
Human-Computer Interaction I.2.1
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin's operation.
Software Engineering
We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to reconstruct protein sequences from tandem mass spectrometry data.
Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.
This thesis report studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.
Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts.