Applies quantum convolutional neural networks to solve partial differential equations on quantum simulators for scientific computing applications.
HEART-PFL framework for personalized federated learning using hierarchical directional alignment and adversarial knowledge transfer to handle data heterogeneity.
UniScale explores synergistic data and model scaling for search ranking, demonstrating that joint architectural and data design improvements outperform model scaling alone.
DVM enables real-time kernel generation for dynamic AI models, addressing compilation overhead and memory footprint issues in runtime compilation.
C-STEP introduces physics-informed safety measures for reinforcement learning in robotics, using intrinsic rewards for safe navigation in continuous domains.
CGRL framework addresses poor generalization of GNNs on out-of-distribution data using causal-guided representation learning to avoid spurious correlations.
Uses hyperspectral imaging and supervised machine learning to map lunar meteorite composition and generate mineralogical maps.
Proposes method to quantify self-awareness in intelligent systems by identifying invariant cognitive processes that change slower than acquired skills.
Neuro-symbolic system using attention-based encoders and differentiable reasoning rules to detect human fatigue from eye-tracking and fNIRS signals.
Two-stage optimization approach for logistics service network design combining metaheuristics, simulation, and machine learning for freight transport.
Investigates joint effects of differential privacy and fairness constraints on federated classification systems across distributed servers.
Studies relationship between fair model representations and fair recommendations in recommender systems, examining demographic attribute classification.
Continuous-time learning framework for probability distributions applied to glucose monitoring in pediatric diabetes clinical trial.
Analysis of why self-distillation degrades LLM reasoning capability by suppressing epistemic verbalization and expression of uncertainty.
Composer 2 model specialized for agentic software engineering with long-term planning and coding abilities trained via continued pretraining and reinforcement learning.
Multi-agent framework with verification for improving calibration and accuracy in medical multiple-choice question answering.
Bayesian optimization method combining penalty formulation and trust region strategy for constrained black-box optimization.
Study evaluating RAG systems on AI policy analysis showing retrieval improvements don't guarantee better answers on complex regulatory documents.
Counterfactual learning approach for conversion rate estimation in recommender systems addressing data sparsity and selection bias.
Inverse-forward differentiation method to reduce memory requirements for backpropagation by avoiding activation storage.
Learning-theoretic framework for coded computing in distributed systems to handle slow, faulty, or compromised servers.
Visualization technique for understanding RNN internal dynamics during training using multislice PHATE algorithm.
Statistical method for heterogeneous treatment effect estimation using local proximity constraints in observational data.
Physics-informed neural networks using wavelet decomposition to improve training on differential equations with rapid oscillations and steep gradients.
arXiv paper on Symmetry-Guided Memory Augmentation (SGMA) improving efficiency of RL-based legged locomotion training.
arXiv paper on machine learning techniques to detect and localize power/radiation leakage of cryptographic keys from hardware implementations.
arXiv paper on multi-agent reinforcement learning for adaptive traffic signal control in heterogeneous urban networks.
arXiv paper: GraphOmni benchmark framework evaluating LLM reasoning on graph-theoretic tasks with diverse formats and serializations.
arXiv paper introducing Distance Explainer method for post-hoc interpretability of embedded vector spaces in ML models.
arXiv paper on Bottlenecked Transformers: KV cache consolidation technique for scaling inference-time reasoning in LLMs.
arXiv paper interpreting neural networks as dynamical systems on latent manifolds, analyzing autoencoder vector fields.
arXiv paper on scalable longitudinal patient pathway modeling from multimodal EHR data using neural networks for condition forecasting.
Research paper demonstrating LLMs perform in-context reinforcement learning during inference. ICRL prompting framework enables inference-time self-improvement.
TimeRecipe benchmarks module-level effectiveness of components in time-series forecasting architectures.
Brain foundation model with Cauchy-Schwarz divergence for cross-subject motor imagery EEG decoding in BCIs.
Classification framework using symbolic dynamics, chaotic maps, and data compression for pattern recognition.
DART adds server-side robustness to federated learning for edge devices without expensive client-side computation.
TimeAlign uses contrastive learning and representation alignment for time series forecasting by bridging input-target distributions.
Theoretical analysis of federated distillation with weighted aggregation of client predictions under class mismatch.
Alternative classification approach using signal separation and trigonometric polynomial kernels for compact metric spaces.
PromptLoop refines prompts for diffusion models using sequential reinforcement learning feedback during sampling.
Generative method for synthetic financial time series data to address data shortage in ML models for trading and investment.
Physics-informed neural network for recovering Raman spectra from CARS measurements using scientific theory as inductive bias.
Develops score-based density estimation from pairwise comparisons for learning from human feedback and expert knowledge elicitation.
Proposes future summary pretraining for LLMs as alternative to next-token prediction, addressing limitations in long-horizon reasoning and planning tasks.
Addresses distribution shift in time-series forecasting by identifying concept drift and temporal shift, proposing mitigation strategies for generalization.
OffSim proposes model-based offline inverse RL framework to learn environmental dynamics and reward functions from offline data without manual definition.
MedM2T is a multimodal framework integrating sparse time series encoding and hierarchical fusion for healthcare data with electronic health records and ECG signals.
SigmaDock uses fragment-based SE(3) diffusion for molecular docking in drug discovery, improving upon generative approaches with better chemical plausibility.
Applies deep RL to dynamic origin-destination matrix estimation in traffic simulations, addressing credit assignment across temporal vehicle dynamics.