AI Applications

Zero-shot Neural ODEs enable autonomous systems to rapidly adapt to unseen scenarios by learning a space of dynamical systems spanned by neural ODE basis functions, achieving efficient online adaptati…

xMIL-LRP: Enhanced explainable AI for multiple instance learning in histopathology, boosting model transparency and enabling new knowledge discovery.

Researchers developed a causally-motivated approach for ranking agents based on their gaming propensity, addressing the challenge of identifying ‘worst offenders’ in strategic classification settings.

VLMimic: Vision-Language Models enable robots to master intricate actions using only a few human video demonstrations, surpassing existing methods by a significant margin.

Vista: a novel driving world model achieving high-fidelity prediction and versatile controllability, outperforming state-of-the-art models in generalization and prediction accuracy.

VidMan: a novel framework leverages video diffusion models and a two-stage training mechanism to significantly improve robot manipulation precision by effectively using robot trajectory data and impli…

VeXKD: A versatile framework boosts 3D perception by cleverly combining cross-modal fusion and knowledge distillation, improving single-modal student model accuracy without extra inference time.

VDD distills complex diffusion policies into efficient Mixture of Experts (MoE) models via variational inference, enabling faster inference and improved performance in behavior learning.

Boosting clinical trial efficiency, this research introduces a covariate-adjusted response-adaptive randomization (CARA) design that effectively leverages surrogate outcomes to handle delayed primary …

UrbanKGent: A unified LLM agent framework revolutionizes urban knowledge graph construction, achieving significantly improved accuracy and efficiency.

MapUnveiler: a novel paradigm for online vectorized HD map construction that leverages clip-level token interaction and propagation to unveil hidden map elements and achieve state-of-the-art performan…

Two-Stage Backdoor Defense (TSBD) unveils and mitigates backdoor vulnerabilities by cleverly unlearning weight changes and suppressing backdoor neuron activeness, significantly improving the robustnes…

Fast disease progression inference is achieved via optimal transport, enabling high-dimensional, interpretable models and offering broad clinical applications.

AI can unlock the potential of global human expertise for solving complex problems by combining diverse expert solutions using an evolutionary framework, resulting in better and more effective strateg…

UniMTS, a novel unified pre-training model, achieves state-of-the-art performance in motion time series classification by generalizing across diverse device factors and activities.

MPI: A novel framework harnesses multi-modal biological data via view decoupling for superior phenotype imputation.

UniGuide: A unified framework for geometry-conditioned molecular generation using unconditional diffusion models, enabling flexible conditioning without extra training or networks.

TurboHopp: 30x faster 3D scaffold hopping with consistency models, boosting drug discovery!

Simulation-free Neural SDE training via Trajectory Flow Matching unlocks scalability and stability for modeling complex real-world time series, particularly in clinical settings.

TrajCLIP: a novel pedestrian trajectory prediction method using contrastive learning and idempotent networks to achieve state-of-the-art performance and enhance generalization across diverse scenarios…