2385 papers
This paper provides a comprehensive overview of Automated Reinforcement Learning (AutoRL), surveying existing literature including recent LLM-based techniques, discussing promising non-tailored methods for future integration, and outlining current challenges and research directions in automating MDP modeling, algorithm selection, and hyper-parameter optimization.
This paper proposes an uncertainty-aware, explainable-by-design subsequence-based model that achieves state-of-the-art classification performance for astronomical uncertain time series by incorporating data uncertainty as an input, thereby enabling domain experts to inspect predictions and potentially inspire new theoretical astrophysics developments.
This survey reviews the recent advances in Large Language Models (LLMs) by covering their background, key findings, and mainstream techniques across four major aspects: pre-training, adaptation tuning, utilization, and capacity evaluation, while also summarizing available resources and discussing future challenges.
This survey reviews the application of utility theory to cognitive modeling in robotics, tracing its evolution from behavior-based approaches to value systems that guide decision-making, learning, and cooperation in single and multi-agent environments, while identifying current limitations and proposing future research directions.
This paper proposes and validates a loop-based algorithm for the online, conflict-free scheduling and routing of automated guided vehicles in pickup and delivery systems, demonstrating its superior or comparable performance to exact, greedy, and metaheuristic methods in terms of solution quality and computational efficiency.
This paper introduces Agent-OM, a novel framework leveraging Siamese LLM agents and specialized tools to address ontology matching challenges, demonstrating competitive performance on simple tasks and significant improvements on complex and few-shot scenarios compared to state-of-the-art systems.
This paper presents a machine learning-focused survey of Computerized Adaptive Testing (CAT), exploring how ML techniques can optimize measurement models, question selection, bank construction, and test control to create more robust, fair, and efficient adaptive assessment systems across various domains.
This paper introduces Fast Explanation (FEX), a novel framework that utilizes policy gradient optimization to represent attribution-based explanations as probability distributions, achieving over 97% reduction in inference time and 70% less memory usage compared to traditional model-agnostic methods while maintaining high-quality, scalable explanations for image and text classification tasks.
This paper identifies a "corruption stage" in few-shot fine-tuned diffusion models caused by a narrowed learning distribution and proposes a Bayesian Neural Network approach with variational inference to broaden this distribution, thereby mitigating corruption and improving image fidelity, quality, and diversity without additional inference costs.
LAMBDA is a novel, open-source, code-free multi-agent system that leverages large language models with collaborative programmer and inspector roles, along with a knowledge integration mechanism and user intervention capabilities, to enhance the accessibility and efficiency of data analysis for diverse users.
The paper proposes OTAD, a novel two-step defense framework that combines optimal transport theory with convex integration to train deep neural networks that achieve both high accuracy and certified local Lipschitz robustness against adversarial attacks.
This paper proposes a novel Variational Learning framework for Gaussian Process Latent Variable Models that utilizes Stochastic Gradient Annealed Importance Sampling to overcome proposal distribution challenges in high-dimensional spaces, achieving tighter variational bounds and superior performance compared to state-of-the-art methods.
This paper introduces the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel framework that integrates audio keyword spotting with diffusion-refined video segmentation to automatically extract key metrics and features for the objective assisted diagnosis of Vocal Fold Paralysis.
This paper explores how integrating artificial intelligence into software testing transforms the generation and validation of test cases by enhancing efficiency, accuracy, and scalability while addressing challenges related to data quality, model transparency, and the balance between automation and human oversight.
This paper challenges the prevailing assumption of Spiking Neural Networks' inherent energy superiority by introducing a rigorous, fair-comparison framework that reveals SNNs only outperform Quantized ANNs under specific low-spike-rate conditions, while demonstrating that such optimized SNNs could nearly double the battery life of devices like smartwatches.
This paper introduces a novel Coupled Oscillator Network (CON) model that overcomes key limitations in latent-space control by ensuring Lagrangian structure, global input-to-state stability, and an invertible input-force mapping, thereby enabling efficient closed-form control strategies for complex mechanical systems using only raw visual feedback.
This paper introduces BNEM, a robust diffusion-based sampler that learns from energy functions via bootstrapped noised energy matching to efficiently generate independent samples from Boltzmann distributions, outperforming existing methods on complex molecular dynamics benchmarks.
The paper proposes PiVOT, a visual prompting mechanism that leverages a pretrained CLIP foundation model to automatically generate and refine online visual prompts, thereby enhancing generic object tracking by effectively suppressing distractors through contrastive guidance.
This paper introduces a constant-lag Neural Delay Differential Equations (NDDEs) framework, inspired by the Mori-Zwanzig formalism, to effectively learn non-Markovian dynamics from partially observed data by identifying memory effects through time delays, demonstrating superior performance over existing methods like LSTMs and ANODEs across synthetic, chaotic, and experimental datasets.
The paper introduces Puppet-CNN, a framework that models convolutional layer parameters as states evolving within a learned neural ODE, enabling input-adaptive computation and significant parameter reduction by dynamically determining the effective network depth based on input complexity.