2 citations · 3 across the 6 of their papers we have counts for
6 papers
Deep Convolutional Tables: Deep Learning without Convolutions
Shay Dekel, Yosi Keller, Aharon Bar-Hillel
We propose a novel formulation of deep networks that do not use dot-product neurons and rely on a hierarchy of voting tables instead, denoted as Convolutional Tables (CT), to enabl…
Inference Graphs for CNN Interpretation
Yael Konforti, Alon Shpigler, Boaz Lernerand Aharon Bar-Hillel
Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. However, the inference process through intermediate layers is opaque, making it d…
Transfer Learning of Photometric Phenotypes in Agriculture Using Metadata
Dan Halbersberg, Aharon Bar Hillel, Shon Mendelson +3
Estimation of photometric plant phenotypes (e.g., hue, shine, chroma) in field conditions is important for decisions on the expected yield quality, fruit ripeness, and need for fur…
Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images
Yuval Litvak, Armin Biess, Aharon Bar-Hillel
Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new…
Convolutional Tables Ensemble: classification in microseconds
Aharon Bar-Hillel, Eyal Krupka, Noam Bloom
We study classifiers operating under severe classification time constraints, corresponding to 1-1000 CPU microseconds, using Convolutional Tables Ensemble (CTE), an inherently fast…
Efficient Human Computation
Ran Gilad-Bachrach, Aharon Bar-Hillel, Liat Ein-Dor
Collecting large labeled data sets is a laborious and expensive task, whose scaling up requires division of the labeling workload between many teachers. When the number of classes…