Search Results for author:
Found 8 papers, 5 papers with code
ReWiTe: Realistic Wide-angle and Telephoto Dual Camera Fusion Dataset via Beam Splitter Camera Rig
The fusion of images from dual camera systems featuring a wide-angle and a telephoto camera has become a hotspot problem recently.
FoodSAM: Any Food Segmentation
Remarkably, this pioneering framework stands as the first-ever work to achieve instance, panoptic, and promptable segmentation on food images.
Ranked #1 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards
To solve these tasks efficiently, we propose a novel self-guided continual RL framework, RelayHER (RHER).
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative.
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, and it also has a superior tolerance of system uncertainty.
Network Density of States
Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators.
Social and Information Networks Numerical Analysis
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.
Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.
