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A Unified Framework for Rank-based Loss Minimization
The empirical risk function is the cornerstone of machine learning.
Offline Multitask Representation Learning for Reinforcement Learning
We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning.
The geometry of hidden representations of large transformer models
By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks.
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion Shitao T ang
MVDiffusion processes perspective images in parallel with a pre-trained text-to-image diffusion model, while integrating novel correspondence-aware attention layers to facilitate cross-view interactions.
DualDefense: EnhancingPrivacyandMitigating PoisoningAttacksinFederatedLearning
DDFedsimultaneously boosts privacyprotection andmitigatespoisoning attacks, without introducing new participant roles or disrupting the existing FL topology.DDFedinitially leveragescutting-edge fullyhomomorphic encryption (FHE)tosecurely aggregatemodelupdates, without theimpractical requirement for non-colluding two-server setups and ensures strong privacy protection.
VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization
Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to
Supplementary Material for " Differentiable Registration of Images and LiDAR Point Clouds with V oxelPoint-to-Pixel Matching " Junsheng Zhou 1 Baorui Ma
We provide the detailed network structure of pixel branch in Figure 1.(c).