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Found 10 papers, 1 papers with code
You Cannot Do That Ben Stokes: Dynamically Predicting Shot Type in Cricket Using a Personalized Deep Neural Network
The ability to predict what shot a batsman will attempt given the type of ball and match situation is both one of the most challenging and strategically important tasks in cricket.
Improved Structural Discovery and Representation Learning of Multi-Agent Data
Central to all machine learning algorithms is data representation.
Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis
Sporting events are extremely complex and require a multitude of metrics to accurate describe the event.
Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders
Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e. g., autonomous vehicles, security, surveillance, sports).
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
Generating Long-term Trajectories Using Deep Hierarchical Networks
We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple coordinating agents.
Predicting Shot Making in Basketball Learnt from Adversarial Multiagent Trajectories
In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories.
Softstar: Heuristic-Guided Probabilistic Inference
Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself.
Representing and Discovering Adversarial Team Behaviors Using Player Roles
In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain.
