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LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
Forecasting the future behaviors of dynamic actors is an important task ...
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LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Self-driving vehicles need to anticipate a diverse set of future traffic...
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Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
In this paper, we address the important problem in self-driving of forec...
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A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
In this paper, we derive generalization bounds for the two primary class...
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GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
In this paper, we propose a geometric neural network with edge-aware ref...
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DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDN...
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Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicle...
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Learning Lane Graph Representations for Motion Forecasting
We propose a motion forecasting model that exploits a novel structured m...
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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately p...
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Fast and Accurate: Structure Coherence Component for Face Alignment
In this paper, we propose a fast and accurate coordinate regression meth...
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Latent Variable Modelling with Hyperbolic Normalizing Flows
The choice of approximate posterior distributions plays a central role i...
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Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation
Feedforward computations, such as evaluating a neural network or samplin...
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Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
In this paper, we tackle the problem of relational behavior forecasting ...
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Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Self-driving vehicles plan around both static and dynamic objects, apply...
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Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In this paper, we propose the differentiable mask-matching network (DMM-...
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Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors. To bor...
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Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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DARNet: Deep Active Ray Network for Building Segmentation
In this paper, we propose a Deep Active Ray Network (DARNet) for automat...
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UPSNet: A Unified Panoptic Segmentation Network
In this paper, we propose a unified panoptic segmentation network (UPSNe...
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LanczosNet: Multi-Scale Deep Graph Convolutional Networks
We propose the Lanczos network (LanczosNet), which uses the Lanczos algo...
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Incremental Few-Shot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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Inference in Probabilistic Graphical Models by Graph Neural Networks
A useful computation when acting in a complex environment is to infer th...
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Reviving and Improving Recurrent Back-Propagation
In this paper, we revisit the recurrent back-propagation (RBP) algorithm...
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Learning deep structured active contours end-to-end
The world is covered with millions of buildings, and precisely knowing e...
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Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph...
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Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Careful tuning of the learning rate, or even schedules thereof, can be c...
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Situation Recognition with Graph Neural Networks
We address the problem of recognizing situations in images. Given an ima...
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Detail-revealing Deep Video Super-resolution
Previous CNN-based video super-resolution approaches need to align multi...
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Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Normalization techniques have only recently begun to be exploited in sup...
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