226 citations · 729 across the 62 of their papers we have counts for
7 papers · 2 filters
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
Aditya Balu, Thanh V. Nguyen, Apurva Kokate +2
We introduce a new, systematic framework for visualizing information flow in deep networks. Specifically, given any trained deep convolutional network model and a given test image,…
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Sambit Ghadai, Aditya Balu, Adarsh Krishnamurthy +1
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of thes…
Interpretable Deep Learning applied to Plant Stress Phenotyping
Sambuddha Ghosal, David Blystone, Asheesh K. Singh +3
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and…
Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang, Aditya Balu, Chinmay Hegde +1
There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model para…
A Machine-Learning Framework for Design for Manufacturability
Aditya Balu, Sambit Ghadai, Gavin Young +2
this is a duplicate submission(original is arXiv:1612.02141). Hence want to withdraw it
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
Adedotun Akintayo, Soumik Sarkar
This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale…
Energy Prediction using Spatiotemporal Pattern Networks
Zhanhong Jiang, Chao Liu, Adedotun Akintayo +2
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dy…