841 citations · 7.7k across the 158 of their papers we have counts for
25 papers · 1 filter
Cost-Efficient Online Hyperparameter Optimization
Jingkang Wang, Mengye Ren, Ilija Bogunovic +2
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…
Adversarial Attacks On Multi-Agent Communication
James Tu, Tsunhsuan Wang, Jingkang Wang +3
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributi…
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
Renjie Liao, Raquel Urtasun, Richard Zemel
In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPG…
LoCo: Local Contrastive Representation Learning
Yuwen Xiong, Mengye Ren, Raquel Urtasun
Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and…
Hierarchical Verification for Adversarial Robustness
Cong Han Lim, Raquel Urtasun, Ersin Yumer
We introduce a new framework for the exact point-wise robustness verification problem that exploits the layer-wise geometric structure of deep feed-forward networks with r…
Learning to Remember from a Multi-Task Teacher
Yuwen Xiong, Mengye Ren, Raquel Urtasun
Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task. In this paper we argue…
Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao, Yujia Li, Yang Song +6
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nod…
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun +1
We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the trid…
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…
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…
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the inc…
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Mengye Ren, Renjie Liao, Raquel Urtasun +2
Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. Thi…
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander G. Schwing, Richard S. Zemel +1
Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the appl…
Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler +1
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate…
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
Patrick Judd, Jorge Albericio, Tayler Hetherington +4
This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study consid…
Learning Deep Structured Models
Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille +1
Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to e…
Blending Learning and Inference in Structured Prediction
Tamir Hazan, Alexander Schwing, David McAllester +1
In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks,…
Efficient Structured Prediction with Latent Variables for General Graphical Models
Alexander Schwing, Tamir Hazan, Marc Pollefeys +1
In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector ma…
Multi-View Learning in the Presence of View Disagreement
C. Christoudias, Raquel Urtasun, Trevor Darrell
Traditional multi-view learning approaches suffer in the presence of view disagreement,i.e., when samples in each view do not belong to the same class due to view corruption, occlu…