Stock prediction framework using autoencoders and transformers with reinforcement learning for adaptive market regime detection.
Hierarchical Bayesian model for online latent-cause inference balancing generalization and discrimination in learning.
SHAPCA: interpretability framework combining SHAP and PCA for explainable ML on high-dimensional spectroscopy data.
Continual learning method using random projection layers with pretrained models for improved representation learning.
DyMoE: dynamic expert selection with mixed-precision quantization for efficient MoE model inference on edge devices.
SOL-ExecBench: 235-problem benchmark for CUDA kernel optimization against hardware efficiency limits for agentic AI systems.
MIDST challenge evaluating membership inference attacks on synthetic tabular data generated by diffusion models.
Statistical method for improving treatment effect estimation by aligning RCTs and observational studies under covariate mismatch.
Security analysis of phishing detectors examining evasion costs and robustness under adversarial feature manipulation.
Online learning algorithms for sequential decision-making with ranking feedback instead of numeric utility feedback.
CONSTRUCT method for real-time trustworthiness scoring of LLM structured outputs and field-level error detection.
MineDraft framework for batch parallel speculative decoding to accelerate LLM inference by hiding draft and verify stages.
Differentiable rendering technique for RF digital twins enabling gradient-based optimization of radio frequency systems.
Sensor fusion method combining UWB and inertial measurement for indoor localization under non-line-of-sight conditions.
Corpus poisoning attacks and defenses for RAG systems, demonstrating vulnerabilities in LLM-extended retrieval pipelines.
Training technique applying sharpness-aware minimization to spiking neural networks using surrogate gradient methods.
Medical imaging method combining intuitionistic fuzzy logic with U-Net architectures for MRI brain image segmentation.
FPGA-based SoC architecture for spiking neural networks using RISC-V controller and event-driven computation for edge AI.
Digital RTL architecture implementing predictive coding networks as alternative to backpropagation for distributed hardware learning.
Framework integrating deep generative models and normalizing flows to accelerate replica exchange molecular simulations.
Foundation diffusion model for computational pathology and histopathology image analysis with self-supervised learning.
Quantization-aware drift correction method for diffusion model sampling to reduce degradation from post-training quantization noise.
Few-shot learning adapter for CLIP using patch-level and text supervision without increasing inference costs.
Defense mechanism against backdoor attacks in audio/speech models using stability-based trigger detection at inference time.
Alternative training architecture for AI models using non-standard arithmetic and memory management for geometric and neuromorphic AI.
Transfer learning for pricing and assortment optimization across markets using multinomial logit choice models with bandit feedback.
Insight-V++ framework enables multi-agent visual reasoning for MLLMs with long-chain reasoning, addressing data scarcity and training optimization.
MAED detects activation errors in DNN inference to mitigate physical fault attacks on embedded neural networks.
Theoretical study on optimal sample complexity for learning unknown bosonic Gaussian quantum states in continuous-variable systems.
Learning-augmented algorithm framework using online learning to inform solutions for k-median clustering problems.
Theoretical analysis proving convergence of ResNet training dynamics in large-scale limits across depth, width, and embedding dimension.
Study of data preparation pitfalls in insurance modeling, highlighting instability of standard train-test splitting on imbalanced data.
Hybrid diffusion-DeepONet framework predicts stress fields in hyperelastic materials with improved handling of sharp gradients.
Study on how ASR quality impacts Alzheimer's disease detection from speech transcripts using lexical feature modeling.
ChoiceEval framework audits brand and cultural preference biases in LLMs to assess market fairness and information diversity risks.
Neural Architecture Search applied to NeRF models for efficient satellite scene 3D reconstruction with reduced training time.
MemArchitect adds governance layer for LLM agent memory management, handling contradictions, privacy, and outdated information in persistent RAG systems.
VCoT-Bench evaluates LLMs on Rust program verification via chain-of-thought reasoning, testing logical deduction abilities beyond binary pass/fail.
Method for reliable uncertainty quantification in Vision-Language-Action models by shifting focus to safety-critical moments in robotic control.
Dataset for detecting human situational awareness gaps in remote human-robot teaming through multimodal sensor data.
PowerFlow applies principled distribution matching to unsupervised reinforcement learning from LLM internal feedback without external supervision.
Theoretical study of computational and statistical hardness in computing calibration distance for probabilistic predictor evaluation.
Research on estimating causal representations from multi-domain data using empirical Bayes methods for causal representation learning.
TARo enables frozen LLMs to perform structured reasoning at inference time through token-level adaptive routing, avoiding expensive post-training alignment.
Statistical framework for quantifying reliability of results from data analysis pipelines using selective inference techniques.
Unsupervised discovery of transition-structure concepts in text via temporal co-occurrence patterns using contrastive learning on large corpus.
Adaptive context allocation method for LLM long-context inference using uncertainty-triggered token-level budgeting to address attention dilution.
Vision-language model method for temporal out-of-distribution detection and domain generalization in open-world settings using adaptive pattern matching.
Analysis of how standard LLM decoding strategies (top-k, nucleus sampling) exclude contextually appropriate but statistically rare tokens compared to human language production.
Theoretical analysis of linear denoisers for noisy data, studying performance in proportional regime without known covariance.