CRIT: Graph-Based Automatic Data Synthesis to Enhance Cross-Modal Multi-Hop Reasoning
arXiv paper on CRIT, graph-based automatic data synthesis for cross-modal multi-hop reasoning. Generates complementary image-text data.
arXiv paper on CRIT, graph-based automatic data synthesis for cross-modal multi-hop reasoning. Generates complementary image-text data.
arXiv paper on label shift estimation with incremental prior updates. Addresses distribution mismatch between training and deployment.
arXiv paper on coupled query-key dynamics for scaled dot-product attention. Improves language modeling perplexity by 6-7% on WikiText-103.
arXiv paper introducing MiCA, parameter-efficient LLM fine-tuning method adapting minor singular vector subspaces. Outperforms LoRA on knowledge retention.
arXiv paper on transformer encoder-decoder with multimodal learning for wind structural health monitoring and digital twins.
arXiv paper on MATA-Former for ICU risk prediction using semantic-aware temporal alignment. Clinical-logic-aligned transformer architecture.
arXiv paper applying Koopman operator methods for multivariable control of turbofan engines. Meta-heuristic extended dynamic mode decomposition.
arXiv paper on DDCL, differentiable end-to-end framework for unsupervised prototype-based representation learning. Integrates feature learning with clustering.
arXiv paper proposing FourierMoE for parameter-efficient LLM fine-tuning in multi-task settings. Mixture-of-experts approach addressing task interference.
arXiv paper integrating deep learning forecasting with integer linear programming for supply chain analytics. Three-stage framework on 180K transactions.
arXiv paper on graph neural operators for real-time virtual sensing on irregular grids with edge deployment. Addresses sparse-to-dense field inference.
arXiv paper combining reinforcement learning with physics-informed priors (Gibbs) for power grid topology control. Semi-Markov framework.
arXiv paper on test-time adaptation for multivariate time-series anomaly detection under distribution shift. Curated adaptation framework (CANDI).
arXiv paper introducing diffusion-based posterior sampling for uncertainty quantification in industrial data-driven models. Addresses safety-critical applications.
arXiv paper on spectral graph contrastive learning addressing high-frequency signal variance in heterophilic graphs. Theoretical analysis with regret bounds.
Self-organising transformer with hierarchical prototype structure that automatically determines optimal architecture dimensions (heads, depth, width) during training.
Layer-wise Interactive Dual-Stream Network architecture for EEG decoding in brain-computer interfaces with improved temporal-spatial feature fusion.
Expert-guided uncertainty modeling for medical AI systems to improve reliability and enable human experts to prioritize high-risk diagnostic cases.
Theoretical analysis of plasticity loss in deep reinforcement learning due to non-stationarity, proposing sample weight decay mitigation technique.
PAC-Bayesian framework for outcome weighted learning that incorporates reward uncertainty into policy selection with finite-sample guarantees.
annbatch: Mini-batch loader for terabyte-scale biological data training in anndata format, addressing memory bottlenecks in ML pipelines for bioinformatics.
LSCP: Self-gated post-training framework for autonomous knowledge acquisition using self-generated Q&A chains and adaptive learning rates based on model conviction.
Statistical analysis of multi-task and multiple operator learning architectures with generalization bounds and theoretical guarantees.
Self-improving world models using forward-inverse asymmetry to improve robustness across suboptimal actions for policy evaluation and planning.
RL post-training framework for building general-purpose reasoning models across diverse domains with verifiable rewards, addressing multi-domain optimization challenges.
Active learning method using feature weighting for regression tasks to optimize sample selection from unlabeled pools under budget constraints.
ArXiv paper on dynamic weight generation for recursive transformers using input-conditioned LoRA modulation controller.
ArXiv paper on fast SVD-based compression for large language models without retraining, addressing distribution shifts.
ArXiv paper applying shallow recurrent decoder networks to magnetohydrodynamic flow modeling in fusion reactors.
ArXiv paper using neural networks to solve inverse problem of designing reflectors for light distribution.
ArXiv paper on graph attention network for multi-sensor object fusion and tracking in autonomous driving.
ArXiv paper introducing Universal Hypernetworks that generate weights for arbitrary model architectures using descriptors.
ArXiv paper applying diffusion denoising objectives to causal structure learning from observational data.
ArXiv paper on model-based reinforcement learning for control systems with time-varying dynamics.
ArXiv paper on in-context agentic reinforcement learning enabling LLM agents to internalize skills at inference time.
ArXiv paper on lightweight diffusion transformer for crystal structure generation using subatomic tokenization.
ArXiv paper unifying group-relative and self-distillation policy optimization for LLM post-training with improved credit assignment.
ArXiv paper proposing Head-Calibrated Clipped-Linear Softmax as efficient surrogate for attention softmax in edge inference.
ArXiv paper on exact parameterization of doubly stochastic matrices for learned mixing in neural networks.
Single-stage training paradigm for efficient LLM reasoning that reduces token consumption in chain-of-thought without degrading quality.
Neural-assisted physics-based model for interpretable battery aging prediction via 2D aging fingerprints without additional diagnostics.
Learning-based cooperative coevolution framework addressing heterogeneous large-scale global optimization via adaptive low-dimensional optimizers.
Lightweight deep learning architecture for brain tumor classification from MRI images with comparative analysis of different approaches.
Machine learning optimization applied to solve modular bootstrap equations for exploring 2D conformal field theories.
Physics-informed neural network and finite volume hybrid approach for modeling UAV traffic patterns in 3D anisotropic wind fields.
Evolutionary multi-objective optimization framework for fusing deepfake speech detectors to balance accuracy and system complexity using NSGA-II.
Neural-symbolic framework for discovering constitutive closures in nonlinear PDEs from spatiotemporal data while avoiding spurious physical recovery.
Research on regularizing attention scores in vision transformers using bootstrapping to improve interpretability and reduce noisy attention maps.
Analysis of safety, security, and cognitive risks in world models used for autonomous decision-making in robotics, autonomous vehicles, and agentic AI systems.
Neural architecture for predicting odorant intensity perception by combining graph convolutional networks with domain-informed design for molecular structure analysis.