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Found 6 papers, 3 papers with code
InfoGaussian: Structure-Aware Dynamic Gaussians through Lightweight Information Shaping
In particular, it can be challenging to animate and move objects in the scene, which requires coordination among many Gaussians.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!
All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training.
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning
However, since the local data is inaccessible to the server under federated learning, attackers may easily poison the training data of the local client to build a backdoor in the agent without notice.
Universal Prompt Tuning for Graph Neural Networks
In this paper, we introduce a universal prompt-based tuning method called Graph Prompt Feature (GPF) for pre-trained GNN models under any pre-training strategy.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known.
