PARE combines structure-aware width pruning and timestep-conditioned adaptive depth routing to cut video diffusion compute while preserving generation quality.
Standard uniform diffusion training uses a leave-one-out posterior rather than the true denoising posterior, causing a parameterization-objective mismatch that new conversions, samplers, and an absorbing-state reformulation fix to match masked diffusion.
PEIRA introduces a non-contrastive self-supervised objective via linear regressor traces whose only stable equilibria recover canonical correlation subspaces, matching VICReg and LeJEPA performance.
Argus detects backdoor attacks in decentralized learning by having nodes share local trigger analyses with neighbors and filter updates via structural similarity, reducing attack success by up to 90 points without a central server.
STRABLE introduces 108 real-world string-and-number tables and benchmarks 445 pipelines, finding simple embeddings with advanced learners suffice for categorical tables while LLMs help on free-text tables.
MulTaBench benchmarks 40 multimodal tabular datasets and shows target-aware tuning of text and image embeddings improves predictive performance over frozen embeddings.
Sequential membership inference attacks exploit model update sequences and canary insertion timing to achieve tighter privacy audits with higher attack power than single-model baselines.