841 citations · 7.7k across the 158 of their papers we have counts for
10 papers · 1 filter
SurfConv: Bridging 3D and 2D Convolution for RGBD Images
Hang Chu, Wei-Chiu Ma, Kaustav Kundu +2
We tackle the problem of using 3D information in convolutional neural networks for down-stream recognition tasks. Using depth as an additional channel alongside the RGB input has t…
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya +5
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a…
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel Urtasun
Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitivel…
End-to-end Learning of Multi-sensor 3D Tracking by Detection
Davi Frossard, Raquel Urtasun
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal,…
Learning deep structured active contours end-to-end
Diego Marcos, Devis Tuia, Benjamin Kellenberger +4
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building fo…
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt, Daniel Tarlow +3
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating i…
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang +1
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit t…
Inference in Probabilistic Graphical Models by Graph Neural Networks
KiJung Yoon, Renjie Liao, Yuwen Xiong +5
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probab…
Reviving and Improving Recurrent Back-Propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya +5
In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We s…
SBNet: Sparse Blocks Network for Fast Inference
Mengye Ren, Andrei Pokrovsky, Bin Yang +1
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computatio…