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Found 24 papers, 5 papers with code
Growing Adaptive Multi-hyperplane Machines
Adaptive Multi-hyperplane Machine (AMM) is an online algorithm for learning Multi-hyperplane Machine (MM), a classification model which allows multiple hyperplanes per class.
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing
While inferring common actor states (such as position or velocity) is an important and well-explored task of the perception system aboard a self-driving vehicle (SDV), it may not always provide sufficient information to the SDV.
Convolutions for Spatial Interaction Modeling
In many different fields interactions between objects play a critical role in determining their behavior.
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Detection of surrounding objects and their motion prediction are critical components of a self-driving system.
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models
Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem.
Ellipse Loss for Scene-Compliant Motion Prediction
Motion prediction is a critical part of self-driving technology, responsible for inferring future behavior of traffic actors in autonomous vehicle's surroundings.
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors.
Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization
To address this issue we propose a simple and general representation for temporally continuous probabilistic trajectory prediction that is based on polynomial trajectory parameterization.
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
Our model builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks.
Multi-Modal Trajectory Prediction of NBA Players
National Basketball Association (NBA) players are highly motivated and skilled experts that solve complex decision making problems at every time point during a game.
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors, which allows the autonomous vehicle to safely and effectively plan its future route in a complex world.
Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions
Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people.
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment.
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact.
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle's surroundings.
Proceedings of the 2017 AdKDD & TargetAd Workshop
Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.
Non-linear Label Ranking for Large-scale Prediction of Long-Term User Interests
We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue.
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.
Network-Efficient Distributed Word2vec Training System for Large Vocabularies
Word2vec is a popular family of algorithms for unsupervised training of dense vector representations of words on large text corpuses.
E-commerce in Your Inbox: Product Recommendations at Scale
In recent years online advertising has become increasingly ubiquitous and effective.
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr
As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases.
Social and Information Networks Computers and Society
