AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
AI-CARE metric for evaluating ML models on carbon emissions and energy consumption alongside standard performance metrics.
AI-CARE metric for evaluating ML models on carbon emissions and energy consumption alongside standard performance metrics.
Research on interpretable Graph Neural Networks using symbolic methods to overcome message-passing limitations and Weisfeiler-Lehman expressivity barriers.
Neuro-symbolic framework (NSGGM) for molecule and graph generation combining neural proposals with symbolic guarantees for controllable generation.
MPZCH indexing mechanism for large-scale recommendation systems to mitigate embedding collisions and improve model freshness in embedding tables.
MASPO algorithm for LLM reasoning via reinforcement learning, addressing gradient utilization, probability mass, and signal reliability in trust region mechanisms.
Theoretical framework for training modular LLMs by combining domain-specific experts without heuristic dataset weighting, matching monolithic model performance.
Framework for multi-round human-AI collaboration ensuring AI complements rather than undermines human decision-making via counterfactual harm and complementarity principles.
NEXUS neural architecture for air quality forecasting in Delhi using spatiotemporal data. Achieves R² > 0.94 for CO prediction.
Fine-tuned DeBERTaV3 system for lateral thinking in language models using humor/riddle data on BRAINTEASER task. LLM reasoning research.
Error correcting code based watermarking framework for detecting machine-generated text in language models. LLM safety research.
Game theory research on controlling strongly monotone games via gradient play and generalized Nash equilibrium constraints.
Quantum and classical neural networks for single-pixel imaging classification. Quantum ML research with imaging application.
Theoretical analysis of Temporal Difference learning convergence with linear function approximation. Foundational RL research.
Mamba-based Mixture of Experts architecture for EMG gesture recognition. Machine learning research with practical HCI application.
Conformal prediction method for assessing optimality gaps in constrained optimization for decision-making in complex systems like supply chains.
SEED metric for semantic evaluation of visual brain decoding models, integrating three complementary metrics aligned with human evaluation data.
Evaluation of seven LLMs on health-claim verification across languages and contexts, assessing how linguistic and contextual factors affect accuracy of AI-generated health advice.
Reproduction and improvement of CheXNet for chest X-ray disease classification on NIH ChestX-ray14 dataset with deep learning models.
Research on Gaussian approximations for decentralized federated learning with local SGD, providing asymptotic statistical guarantees for privacy-sensitive distributed optimization.
HoloLLM is a multimodal LLM for embodied agents in smart homes that processes diverse sensory inputs beyond vision for language-grounded perception and human behavior understanding.
Feature selection filter using copula-based tail concordance scoring for diabetes risk prediction model interpretability.
Diffusion model approach for synthesizing discrete-continuous quantum circuits to reduce compilation overhead in quantum computing.
Analysis showing watermarking degrades LLM alignment safety properties and proposes mitigation strategies for deployment compatibility.
Method enabling LLMs to generate and iteratively refine continuous control policies for embodied agent sensory-motor control.
Benchmark dataset of 984K historical Icelandic census records for evaluating identity resolution algorithms across 220 years.
Theoretical analysis of feedback-driven quantum reservoir computing for temporal machine learning on intermediate-scale quantum devices.
Training method enabling LLMs to learn procedural knowledge from declarative instructions, demonstrating instruction efficiency in fine-tuning.
Hybrid approach combining spiking neural networks with quantum computing for neuromorphic AI with temporal data processing.
Theoretical analysis of multinomial logistic bandits with minimax-optimal algorithm addressing non-linearity in feedback mechanisms.
Comparative analysis of State Space Model and hybrid architectures versus Transformers for long-context processing on edge devices.
Study comparing exchangeability assumptions versus i.i.d. assumptions for handling distribution shifts in pooled medical imaging datasets.
LLM-based agent system for schema-guided extraction and recommendation from financial tables with missing structural metadata.
Generative modeling pipeline for peptide discovery that predicts aggregate morphology and generates novel biocompatible sequences.
Policy optimization method addressing credit assignment problems in reinforcement learning for aligning text-to-image generation models.
Deep joint source-channel coding method with hash distillation for semantic image transmission over multi-hop channels.
Framework for automatically generating demonstrations for training multi-step bimanual mobile manipulation robots under soft and hard constraints.
Theoretical framework analyzing convergence properties of graph neural operators using spectral analysis and graphon theory.
Research evaluating security vulnerabilities of backbone LLMs used in AI agents, addressing systematic security modeling for deployed agent systems.
Deep learning approach for calibrating uncertainty estimates in multi-label bird sound classifiers for bioacoustic monitoring.
Research on expert-router coupling loss for mixture-of-experts models to align router decisions with expert capabilities.
Fast-ThinkAct framework for efficient vision-language-action reasoning using compact latent planning to reduce inference latency.
Theoretical proof of universality for many-body quantum machine learning models in approximating quantum distributions.
FROST method for efficient LLM reasoning by pruning uncritical paths using attention weights to reduce inference latency.
Theoretical analysis of privacy amplification in shuffled data analysis beyond local differential privacy bounds.
Self-supervised learning framework applying vision models to cryo-EM density maps for structural biology analysis.
Deep learning model for classifying neoplastic tubular adenomas in colonoscopy screenings using medical image analysis.
Research on object tracking using 3D geometric reasoning and online model editing to handle occlusion and appearance variations.
Technical report on UI-Venus-1.5, a unified GUI agent for automating digital environment interactions with multiple model variants.
Study on synthetic query generation for dense retrieval showing quality-diversity tradeoffs across 31 datasets, benefits multi-hop reasoning.
Research evaluating AI safety datasets, finding they overrely on obvious triggering cues and lack real-world adversarial depth.