Optimization
Norm-guided latent space exploration for text-to-image generation
They map random samples (seeds) from a high-dimensional space, conditioned on a user-provided text prompt, to a corresponding image.
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases.
GloptiNets: Scalable Non-Convex Optimization with Certificates
Non-convex optimization is a difficult and crucial task.
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets.
Bi-Level Offline Policy Optimization with Limited Exploration
Subsequently, at the upper level, the policy aims to maximize a conservative value estimate from the confidence set formed at the lower level.
GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference Ziang Li1, Mengda Y ang
To overcome these challenges, we propose a G AN-based LA tent S pace S earch attack ( GLASS) that harnesses abundant prior knowledge from public data using advanced StyleGAN technologies. Additionally, we introduce GLASS++ to enhance reconstruction stability.
