Search Results for author:
Found 19 papers, 8 papers with code
WHAC: World-grounded Humans and Cameras
In this study, we aim to recover expressive parametric human models (i. e., SMPL-X) and corresponding camera poses jointly, by leveraging the synergy between three critical players: the world, the human, and the camera.
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar
A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging.
Learning Pseudo Front Depth for 2D Forward-Looking Sonar-based Multi-view Stereo
However, owing to the unique image formulation principle, estimating 3D information from a single image faces severe ambiguity problems.
Efficient Video Deblurring Guided by Motion Magnitude
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur.
ReIL: A Framework for Reinforced Intervention-based Imitation Learning
Compared to traditional imitation learning methods such as DAgger and DART, intervention-based imitation offers a more convenient and sample efficient data collection process to users.
Continuous-Depth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuous-depth graph neural networks (GNNs).
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes.
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies.
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods.
TorchDyn: A Neural Differential Equations Library
Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation.
Hypersolvers: Toward Fast Continuous-Depth Models
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilization in problems of non-trivial size has often proved impossible due to poor computational scalability.
360$^\circ$ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron
Our proposed method is robust to camera alignments by using the extrinsic camera parameters; therefore, it can achieve precise depth estimation even when the camera alignment differs from that in the training dataset.
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network.
Port-Hamiltonian Gradient Flows
In this paper we present a general framework for continuous--time gradient descent, often referred to as gradient flow.
Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces
Action spaces equipped with parameter sets are a common occurrence in reinforcement learning applications.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Dissecting Neural ODEs
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs).
Graph Neural Ordinary Differential Equations
We introduce the framework of continuous--depth graph neural networks (GNNs).
Port-Hamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations.
