Multi-scene indoor dataset for human detection, segmentation, and tracking across campus locations with automated annotation pipeline.
Reinforcement learning approach for speculative trading formulated as sequential optimal stopping with Cox process-driven intensity controls.
Mining instance-centric vision-language contexts for human-object interaction detection, leveraging VLMs to improve semantic understanding and contextual reasoning.
LatentUM unified model for interleaved cross-modal reasoning combining visual understanding, generation, and world dynamics in latent space.
CASHG method for context-aware stylized online handwriting generation with stroke continuity and character spacing.
Gradient estimators for parameter inference in discrete stochastic kinetic models, enabling efficient differentiation of stochastic simulation algorithms.
Hybrid framework combining LSTM workload prediction with game-theoretic heuristics for cloud cost optimization during dynamic workload changes.
AEGIS system using thermodynamic state space models for zero-day network evasion detection against adversarial byte-level morphing attacks.
AstroConcepts corpus of 21,702 astrophysics abstracts for multi-label classification research addressing extreme class imbalance with specialized terminology.
Two-stage framework for GPU resource and power prediction in HPC systems using Slurm logs and NVIDIA DCGM metrics.
Shared task participation comparing lexical and contextual approaches for cross-document software mention coreference resolution under mention noise.
Analysis of Mixture-of-Experts LLM interpretability at expert level, comparing sparsity properties to dense feed-forward networks using probing methods.
Characterization of exact Pareto fronts in average-cost multi-objective MDPs, extending prior work on discounted settings.
Method for verifying operational design domain coverage for safety-critical AI systems in aviation, addressing EASA certification requirements.
ASK framework combines smaller language models with RL policies to enhance out-of-distribution generalization by gating LM assistance based on agent uncertainty.
Extended publication on learning state machines from data streams with PAC-bounds analysis and heuristic improvements.
Bayesian vertical federated learning approach for multimodal survival prediction with privacy preservation and uncertainty quantification.
Multi-armed bandit study on best arm identification when agent commands are transmitted over noisy discrete channels with analysis of zero-error capacity.
De Jure is an automated pipeline for extracting structured regulatory rules from legal documents using LLM self-refinement, requiring no human annotation or domain-specific data.
Analysis of token initialization strategies for new vocabulary in language models used for generative recommendation systems.
Video diffusion research on multi-agent action binding in generative video games for world models.
Risk-aware linear bandits optimization with application to smart order routing in financial decision-making.
ML research on double acceleration of distributed optimization through local training and compression in gradient descent.
Theoretical analysis showing transformers can solve non-linear non-Markovian stochastic filtering for conditionally Gaussian signals.
Moonwalk: inverse-forward differentiation technique addressing backpropagation's memory requirements for training deeper networks.
Industrial anomaly detection approach using ExIFFI for fast, interpretable explanations in Industry 5.0 processes.
Comprehensive benchmark of 17 graph pooling methods across 28 datasets evaluating effectiveness, robustness and generalization.
Neural architecture using selective logical operators for interpretable classification with adaptive AND/OR operations.
Study of in-context learning in LLMs with spurious correlations, examining transformer robustness to spurious features in classification.
Automated neural architecture selection for time series forecasting comparing LSTMs, GRUs, Transformers, and State-Space Models.
ML research on classification metrics accounting for confidence in incorrect predictions for safety-critical applications.
Foundation model for single-cell RNA sequencing analysis in disease biology and drug discovery applications.
Scientific ML research on training neural differential-algebraic equation systems extending neural ODE methods.
GradPower: lightweight gradient transformation technique for accelerating LLM pre-training via sign-power transformation, requiring minimal code changes.
ML research introducing Reliable Policy Iteration variants that maintain theoretical guarantees under function approximation in reinforcement learning.
Research on improving CNN interpretability by accounting for softmax dependency on logit differences rather than absolute values in class activation mapping.
Investigation of Generalized Gaussian mechanism for differential privacy in machine learning as alternative to Laplace/Gaussian.
GAN-based residual guided training strategy for Physics-Informed Transformers solving nonlinear PDEs.
Low-rank amortized Bayesian meta-learning for LLMs enabling few-shot learning across multiple datasets efficiently.
TANDEM: attention-guided neural differential equations for time series classification with missing data handling.
DeepIMC: machine learning framework for fast calibration of agent-based epidemic models via bidirectional LSTM.
GCond: scalable gradient conflict resolution for large-scale multi-task learning using accumulation-based stabilization.
StelLA: geometry-aware extension of LoRA using three-factor decomposition on Stiefel Manifold for efficient model fine-tuning.
Reinforcement learning approach for task offloading on Internet of Wearable Things to overcome battery and computation constraints.
Analysis of what temporal graph learning models learn, addressing reliability concerns in benchmark evaluation protocols.
Rigorous approach for determining rank identifiability in tensor factorization using prior predictive moment matching.
Strategic Doubly Robust estimator framework for causal inference in strategic equilibrium systems with endogenous treatment.
Theoretical framework connecting sublinear graph algorithms to test-time LLM methods like RAG and tool use via prior knowledge.
Theoretical analysis of when Transition Matching outperforms Flow Matching for generative models with fewer sampling steps.
Performance metric for binary classifiers with precision and capacity constraints, extending ROC curve analysis.