Search Results for author:
Found 14 papers, 1 papers with code
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces.
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering
State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy.
Meta-Learning for Neural Network-based Temporal Point Processes
The point process is widely used in many applications to predict such events related to human activities.
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems.
Initialization Bias of Fourier Neural Operator: Revisiting the Edge of Chaos
Building upon this observation, we also propose an edge of chaos initialization scheme for FNO to mitigate the negative initialization bias leading to training instability.
Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e. g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e. g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods.
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet.
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Hawkes processes offer a central tool for modeling the diffusion processes, in which the influence from the past events is described by the triggering kernel.
Few-shot Learning for Spatial Regression
We propose a few-shot learning method for spatial regression.
Probabilistic Optimal Transport based on Collective Graphical Models
In this study, we propose a new framework in which OT is considered as a maximum a posteriori (MAP) solution of a probabilistic generative model.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity.
