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06/27/2023
Rethinking Closed-loop Training for Autonomous Driving
Recent advances in high-fidelity simulators have enabled closed-loop tra...
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10/19/2022
Gaussian-Bernoulli RBMs Without Tears
We revisit the challenging problem of training Gaussian-Bernoulli restri...
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10/07/2022
Scaling Forward Gradient With Local Losses
Forward gradient learning computes a noisy directional gradient and is a...
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10/06/2022
Learning to Reason With Relational Abstractions
Large language models have recently shown promising progress in mathemat...
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09/13/2021
Online Unsupervised Learning of Visual Representations and Categories
Real world learning scenarios involve a nonstationary distribution of cl...
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04/08/2021
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Self-driving vehicles must perceive and predict the future positions of ...
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01/17/2021
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
Modern self-driving perception systems have been shown to improve upon p...
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01/17/2021
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibil...
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01/17/2021
Adversarial Attacks On Multi-Agent Communication
Growing at a very fast pace, modern autonomous systems will soon be depl...
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01/16/2021
Self-Supervised Representation Learning from Flow Equivariance
Self-supervised representation learning is able to learn semantically me...
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01/16/2021
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
As self-driving systems become better, simulating scenarios where the au...
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01/16/2021
SceneGen: Learning to Generate Realistic Traffic Scenes
We consider the problem of generating realistic traffic scenes automatic...
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12/10/2020
Flexible Few-Shot Learning with Contextual Similarity
Existing approaches to few-shot learning deal with tasks that have persi...
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11/10/2020
Learning to Communicate and Correct Pose Errors
Learned communication makes multi-agent systems more effective by aggreg...
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11/02/2020
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
In this paper, we propose an end-to-end self-driving network featuring a...
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10/14/2020
Theoretical bounds on estimation error for meta-learning
Machine learning models have traditionally been developed under the assu...
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08/27/2020
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
Sketch drawings are an intuitive visual domain that appeals to human ins...
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08/13/2020
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
In this paper we propose a novel end-to-end learnable network that perfo...
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08/13/2020
End-to-end Contextual Perception and Prediction with Interaction Transformer
In this paper, we tackle the problem of detecting objects in 3D and fore...
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08/04/2020
LoCo: Local Contrastive Representation Learning
Deep neural nets typically perform end-to-end backpropagation to learn t...
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07/09/2020
Multi-Agent Routing Value Iteration Network
In this paper we tackle the problem of routing multiple agents in a coor...
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07/09/2020
Wandering Within a World: Online Contextualized Few-Shot Learning
We aim to bridge the gap between typical human and machine-learning envi...
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04/01/2020
Physically Realizable Adversarial Examples for LiDAR Object Detection
Modern autonomous driving systems rely heavily on deep learning models t...
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10/24/2019
Identifying Unknown Instances for Autonomous Driving
In the past few years, we have seen great progress in perception algorit...
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10/10/2019
Learning to Remember from a Multi-Task Teacher
Recent studies on catastrophic forgetting during sequential learning typ...
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10/10/2019
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
The motion planners used in self-driving vehicles need to generate traje...
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07/30/2019
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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10/16/2018
Incremental Few-Shot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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10/12/2018
Graph HyperNetworks for Neural Architecture Search
Neural architecture search (NAS) automatically finds the best task-speci...
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03/24/2018
Learning to Reweight Examples for Robust Deep Learning
Deep neural networks have been shown to be very powerful modeling tools ...
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03/06/2018
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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03/02/2018
Meta-Learning for Semi-Supervised Few-Shot Classification
In few-shot classification, we are interested in learning algorithms tha...
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01/07/2018
SBNet: Sparse Blocks Network for Fast Inference
Conventional deep convolutional neural networks (CNNs) apply convolution...
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07/14/2017
The Reversible Residual Network: Backpropagation Without Storing Activations
Deep residual networks (ResNets) have significantly pushed forward the s...
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11/14/2016
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Normalization techniques have only recently begun to be exploited in sup...
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05/30/2016
End-to-End Instance Segmentation with Recurrent Attention
While convolutional neural networks have gained impressive success recen...
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05/08/2015
