AI Applications

GAVEL uses AI to evolve novel board games by mutating and recombining game rules encoded in a sophisticated game description language.

GarmentLab: A new benchmark and simulation platform tackles garment manipulation challenges by offering realistic simulations, diverse assets, and tasks bridging the sim-to-real gap for more robust AI…

FUSE, a novel framework, efficiently predicts continuous fields & estimates discrete parameters in PDEs, significantly improving accuracy and robustness.

Boost time series forecasting accuracy by integrating news data and LLM-based agents!

Boost pathology model accuracy with Concept Anchor-guided Task-specific Feature Enhancement (CATE)! This adaptable paradigm enhances feature extraction for specific tasks using task-relevant concepts,…

Fourier Neural Processes (FNP) revolutionizes data assimilation by enabling accurate analysis of observations with varying resolutions, improving weather forecasting and Earth system modeling.

Fine-grained control is added to generative models for IoT sensing data augmentation, tailoring synthetic data to specific application needs by leveraging domain expertise and statistical metrics of s…

FINCON: an LLM-based multi-agent system uses conceptual verbal reinforcement for superior financial decision-making, generalizing well across various tasks.

This paper introduces a novel formalization of feint behaviors in multi-player games, improving AI performance and game diversity via a unified MARL implementation.

FastSurvival unveils computationally efficient methods for training Cox Proportional Hazards models, achieving high precision and overcoming convergence issues of previous algorithms.

FAST, a novel dual-tier few-shot learning paradigm, significantly boosts whole slide image (WSI) classification accuracy by efficiently using limited annotations and all available WSIs.

FACTORSim generates full, coded simulations from natural language descriptions, outperforming existing methods in accuracy and zero-shot transfer learning by using a factored POMDP representation.

KalmanNet, a novel BMI decoder, achieves state-of-the-art performance by integrating recurrent neural networks into Kalman filtering, balancing accuracy and explainability.

This research introduces a novel, automated protocol translation framework for self-driving labs, tackling the challenge of converting human-readable experimental protocols into machine-interpretable …

E-NSDE, a novel time-aware sequential recommendation model, integrates neural stochastic differential equations and evidential learning to improve recommendation accuracy by effectively handling varia…

ET-Flow, a novel equivariant flow-matching model, generates highly accurate and physically realistic molecular conformers significantly faster than existing methods.

Faster, more efficient deep learning for diffusion MRI deconvolution is achieved using spatio-hemispherical networks, improving fiber tractography.

Boosting preference learning, this research uses human response times to improve linear bandit algorithms, significantly accelerating preference identification.

EGODE, a novel framework, leverages coupled graph ODEs and an event module to accurately model continuous and instantaneous changes in rigid body dynamics, outperforming existing methods.

DynaMo: a novel self-supervised method significantly boosts visuo-motor control by learning in-domain dynamics from limited expert demonstrations, improving policy performance across various environme…