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Found 7 papers, 7 papers with code
Unsupervised Learning of Neurosymbolic Encoders
We present a framework for the unsupervised learning of neurosymbolic encoders, which are encoders obtained by composing neural networks with symbolic programs from a domain-specific language.
Deep Learning-based Damage Mapping with InSAR Coherence Time Series
In this study, we propose a novel approach to damage mapping, combining deep learning with the full time history of SAR observations of an impacted region in order to detect anomalous variations in the Earth's surface properties due to a natural disaster.
Task Programming: Learning Data Efficient Behavior Representations
The tasks in our method can be efficiently engineered by domain experts through a process we call "task programming", which uses programs to explicitly encode structured knowledge from domain experts.
Learning Differentiable Programs with Admissible Neural Heuristics
This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.
Learning Calibratable Policies using Programmatic Style-Consistency
We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously.
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems.
Ranked #1 on
Multivariate Time Series Imputation
on PEMS-SF
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.
