Contextual Graph Representations for Task-Driven 3D Perception and Planning
Method for extracting task-relevant contextual representations from 3D scene graphs for robot planning and perception.
Method for extracting task-relevant contextual representations from 3D scene graphs for robot planning and perception.
Learning-to-rank system for recommending charging nodes in peer-to-peer EV energy trading networks.
Generative theory for synthesizing physiologically consistent multimodal ECG data using quantum-inspired approach.
Benchmark dataset for remote pulse wave detection using event cameras and multimodal physiological sensing.
Physics-informed generative model using Mamba architecture for efficient protein backbone design with linear-time complexity and improved structural fidelity.
FEMBA: bidirectional Mamba state-space model pre-trained on 21k hours EEG with physiologically-aware objectives for microcontroller deployment.
Vision Transformer applied to stress classification from ECG signals using STFT spectrograms for physiological signal analysis.
Enhanced mixture-of-experts architecture using soft nearest neighbor loss to prevent expert collapse and redundant representations.
Cross-lingual evaluation of vision-language models on visual reasoning tasks across Indian languages, revealing performance disparities.
Framework integrating sparse autoencoders with dynamic head pruning in Vision Transformers for interpretable and controllable efficiency.
Deep image clustering using Transformers with distribution information fusion to improve clustering on high-dimensional multimedia data.
MotionGPT3 replaces diffusion with rectified flow objectives for efficient text-driven motion generation with improved convergence.
Evolution strategies warm-start reinforcement learning agents for industrial continuous control using CMA-ES-generated demonstrations.
LogicDiff improves reasoning in masked diffusion language models by prioritizing logical connective tokens during inference-time denoising.
Application of cognitive map learners for hierarchical, compositional robot arm control without task-specific retraining.
Deep learning framework for forecasting black hole plasma accretion dynamics from single telescope images using long-term sequence modeling.
Privacy-preserving inference for spiking neural networks using fully homomorphic encryption to enable encrypted computation.
Language-conditioned multi-game procedural level generation using shared neural representations across different game domains.
Adaptation of Firefly Algorithm metaheuristic for optimization problems with mixed continuous, ordinal, and categorical variables.
Deep learning method for genetic biomarker prediction from pathology images using dictionary-based representations and classifier debiasing.
Benchmark evaluating implicit neural representations for zebrafish brain atlas registration and microscopy reconstruction tasks.
Data-driven superresolution technique using conditional normalizing flows to recover fine-grained calorimeter information in high energy physics simulations.
Study of why minimal GPTs fail at out-of-distribution arithmetic generalization, revealing staged failures from layout barriers to positional encoding limits.
Survey of uncertainty-aware explainable AI methods, examining integration of uncertainty quantification (Bayesian, Monte Carlo, Conformal) into explanatory pipelines.
Controlled evaluation of LLM implementation choices for political text annotation, testing model selection, size, and prompt engineering best practices.
Comparative evaluation of physics-informed neural networks and neural ODEs for modeling nonlinear neuronal dynamics on Morris-Lecar model.
KOMET: model-agnostic framework using Koopman operators to track parameter evolution and handle temporal domain drift in non-stationary environments.
Graph-supported barycenter computation for probability measures using Riemannian geometry for signal processing and machine learning applications.
Multimodal deep learning combining RGB and thermal imaging on Raspberry Pi for diabetic foot ulcer staging classification and monitoring.
ASTER: agentic toolkit using LLMs for exoplanet research workflows, combining archival queries, literature search, and radiative transfer models.
Graph attention network classifier for autism spectrum disorder detection using fMRI brain imaging data and graph convolutional networks.
Online statistical inference framework for sample-averaged Q-learning to reduce variance and improve stability in reinforcement learning.
Analysis of reliability limits in LLM-based multi-agent planning systems modeled as decision networks with language-based communication constraints.
FormalProofBench: benchmark evaluating whether LLMs can produce formally verified graduate-level mathematical proofs using Lean 4.
Status update systems for wireless channels with energy constraints, modeling information freshness as expiring coupons for control applications.
Speaker anonymization system that neutralizes non-native accents to sound native-like while preserving timbre for real-time streaming applications.
Lightweight neural network for super-resolution imaging from low-resolution SPAD arrays, reconstructing 256x256 images on embedded devices.
Parameter estimation for stochastic differential equations using Wiener chaos expansion and stochastic gradient descent for computational efficiency.
Comparative study evaluating YOLO object detection models on robotics tasks using custom and COCO2017 datasets for workspace object detection.
Nuclear material identification using X-ray radiography, gamma-ray spectroscopy, and neutron measurements for plutonium sphere detection.
Study of incentive collapse paradox in AI-assisted task delegation showing accuracy improvements require unbounded payments without intervention mechanisms.
Persona-based LLM approach for simulating diverse human opinions at population scale for social science interventions and consequence modeling.
Theoretical analysis of loss landscape geometry in regularized deep matrix factorization proving unique minimizers under weight decay.
Information-theoretic framework for forecasting measuring mutual information between future observations and information set as predictability limit.
Sovereign Context Protocol defines open runtime attribution layer for human-generated content used in LLM training and inference.
Systematic evaluation of segmentation and geospatial foundation models for global field boundary segmentation using FTW benchmark.
Bayes-MICE extends multiple imputation for time series missing data using Bayesian inference and MCMC sampling.
Landmark-guided pose scoring system for automated transducer positioning in point-of-care cardiac ultrasound acquisition.
Pan-cancer immune landscape mapping through metagene clustering and predictive modeling to identify immunotherapy response drivers.
Weakly convex ridge regularizer for 3D non-Cartesian MRI reconstruction providing stable variational alternative to deep learning methods.