Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data
L1-NMF algorithm for nonnegative matrix factorization robust to heavy-tailed noise and outliers with NP-hardness analysis.
L1-NMF algorithm for nonnegative matrix factorization robust to heavy-tailed noise and outliers with NP-hardness analysis.
One-for-All: parameter-efficient LoRA variant (rsLoRA) for adapting frozen LLMs to multivariate time-series forecasting tasks.
Training-free method to combine multiple domain-specific expert LLMs into single multi-domain model without fine-tuning.
Big2Small unifies model compression techniques (pruning, quantization, distillation, decomposition) under single mathematical framework.
Multimodal ML framework predicting metastasis risk from electronic health records across four cancer cohorts.
Uses reduced density matrices from quantum chemistry to predict phase transitions in neural networks during training and improve interpretability.
AMShortcut model for inverse design of amorphous materials using generative models with reduced computational requirements.
Proposes EAGLE, a federated learning algorithm ensuring fair performance across heterogeneous clients by minimizing loss gap parity.
Curvature-Guided LoRA: Parameter-efficient fine-tuning approach using prediction alignment to match full fine-tuning performance.
DiSGMM method for time-varying microscopic weight completion on road networks for traffic simulation.
Study of label leakage problem in relational transfer learning where task scarcity causes models to learn task-specific shortcuts.
ShapPFN: Foundation model integrating Shapley value regression for real-time interpretable predictions on tabular data.
GPT4AP: Parameter-efficient multi-task forecasting framework using rsLoRA for air pollution prediction in data-scarce regions.
Application of InterSHAP to quantify cross-modal interactions in multimodal deep learning for glioma survival prediction.
Target-Weighted Cross-Validation method for improving predictive risk estimation in spatial prediction with structured data.
Framework for discovering and validating mechanistic interpretations across neural networks to improve interpretability and generalization.
Research on Tucker attention as a generalization of approximate attention mechanisms like GQA and MLA using low-rank factorizations.
NeuralUCB-based cost-aware LLM routing algorithm that adapts online to model performance and cost, outperforming supervised routing baselines.
CRAFT: Cost-aware expert replica allocation for mixture-of-experts LLM serving with fine-grained layerwise load-balancing estimations.
Spark-LLM-Eval: Distributed framework for statistically rigorous evaluation of LLMs at scale across hundreds of thousands of samples.
UltRAG: Scalable recipe for knowledge graph RAG with LLMs to reduce hallucinations by integrating structured knowledge in context windows.
Conditional GAN for generating biomaterial microtopography with internally repeated periodic patterns and global structural consistency.
Early warning system for GPU failures using observability and structural signals beyond numeric telemetry for HPC and AI workloads.
Smartphone-based liquid identification using active vibration sensing and machine learning to measure viscosity differences.
Attention-LSTM framework for Kubernetes autoscaling addressing temporal blindness in serverless workload orchestration using deep reinforcement learning.
Foundation model for particle physics detector simulation using mixture-of-experts and parameter-efficient fine-tuning inspired by LLM techniques.
ML-based database parameter tuning system using workload compression to reduce configuration evaluation cost and improve DBMS performance.
Machine learning approach for estimating interfacial Dzyaloshinskii-Moriya interaction strength in magnetic materials from experimental data.
OptiMer: Method to optimize data mixture ratios for LLM continual pre-training by extracting and merging distribution vectors without fixed hyperparameters.
Score calibration method for heterogeneous graph-vector retrieval fusion in multi-hop question answering using percentile-rank normalization.
Multi-agent reinforcement learning approach for unmanned aircraft separation assurance under adversarial GPS degradation and spoofing.
Theoretical analysis of minimum-norm interpolation under 2-uniform convexity assumptions for understanding generalization in overparameterized neural networks.
Multi-agent traffic simulation framework using self-supervised world models to scale autonomous driving system testing with unlabeled sensor data.
Model-based reinforcement learning approach using Pontryagin methods and Hamiltonian actor-critic to address compounding model errors in long-horizon value estimation.
Mimosa: evolving multi-agent framework for autonomous scientific research that synthesizes and refines LLM-based agent workflows.
Transfer learning approach for Bayesian optimization applied to aircraft design problems.
Topological analysis of persistent homology for detecting phase transitions in spin models.
PolarQuant: post-training weight quantization method for LLM compression using Hadamard rotation and Gaussian optimization.
Analysis of modality gaps in multi-modal models like CLIP from robustness perspective.
Neural network approximation for inverse delay mapping in time-varying delay systems via operator learning.
Theoretical analysis of cyclic block coordinate optimization methods for variational inequalities.
Medical imaging segmentation using foundation models like MedSAM for brain tissue classification from MRI data.
GNN-based model for software vulnerability detection that offers better scalability than LLM approaches for code analysis tasks.
LiteCoST framework for document QA using chain-of-structured-thought and fine-tuned small language models for high accuracy and low latency.
Thiomi: large-scale multimodal dataset with 600k+ text annotations and 385k+ audio recordings across 10 African languages.
MemRerank framework distilling user purchase history into preference signals for personalized LLM-based shopping agent product reranking.
CNN and LightGBM surrogate models for real-time electromagnetic transient prediction in inverter-based microgrids.
SABLE framework for semantically-aware backdoor attacks in federated learning using realistic, in-distribution triggers.
Method for generating rigorous, human-interpretable explanations for tree ensemble model predictions.
Hardware-software framework for automatic task partitioning of deep reinforcement learning on Xilinx Versal ACAP.