2316 papers
This paper proposes Adaptive Transfer Clustering (ATC), a unified framework that automatically leverages commonalities between a main and an auxiliary dataset to improve clustering performance despite unknown discrepancies, while providing theoretical optimality guarantees under Gaussian mixture models and demonstrating effectiveness through extensive experiments.
This paper proposes a learned proximal alternating minimization (LPAM) algorithm and its corresponding interpretable network (LPAM-net) for solving two-block nonconvex and nonsmooth optimization problems, proving their convergence to Clarke stationary points and demonstrating superior performance in joint multi-modal MRI reconstruction.
This paper introduces a neurosymbolic system that reconstructs medical images using visual primitives to generate high-level structural explanations, achieving superior classification accuracy and transparency compared to conventional deep learning models in diagnosing histological abnormalities.
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.
This paper proposes an input-adaptive framework for diffusion models that dynamically adjusts the generative trajectory and sampling steps for each sample based on its complexity, thereby maintaining generation quality while reducing the average number of required steps.
This paper introduces a locomotor task set optimization strategy that uses cluster analysis to identify a minimal, representative subset of tasks for training deep learning models, enabling accurate estimation of hip joint moments for exoskeleton control while significantly reducing data collection requirements.
This paper proposes a parametric, finite-dimensional approach for non-parametric regression that achieves minimax-optimal uniform convergence rates for learning smooth functions and their derivatives while significantly reducing memory and computational costs compared to traditional kernel-based methods.
This paper explores the application of Generative Diffusion Models (GDMs) in massive MIMO communications by reviewing their theoretical foundations, analyzing recent advancements including a case study on near-field channel estimation, and outlining future research directions and challenges for enhancing channel state information acquisition in 5G and 6G networks.
This paper investigates the impact of embedding collapse in Prompt-Tuning by introducing embedding priors, revealing that models can effectively utilize embeddings from diverse activation regions and that distinct activation clusters exist for different task types, suggesting controllable posteriors could enhance interpretability and serve as a foundation for tasks like chain-of-thought distillation.
This paper proposes a method that leverages pretrained vision-language models to learn compact, abstract symbolic world models from limited visual demonstrations, enabling zero-shot generalization and long-horizon planning for complex robotic tasks across novel objects, environments, and goals.
UFGraphFR is a novel federated recommendation framework that enhances privacy-preserving personalization by transforming user data into semantic text vectors to reconstruct global user relationship graphs on the server and employing Transformer architectures for behavior sequence modeling, thereby significantly outperforming existing baselines in accuracy and personalization.
This paper proposes efficient data reduction strategies for semi-supervised adversarial training that utilize latent clustering techniques to select or generate critical boundary-adjacent samples, significantly reducing data requirements and computational costs while maintaining state-of-the-art robustness.
This paper derives error bounds for the excess risk of deep neural network classifiers trained on noisy labels by decomposing the risk into statistical and approximation errors, utilizing independent block construction for dependent data and refining results under the low-dimensional manifold hypothesis.
This paper introduces Generative Predictive Control (GPC), a framework that enhances robotic manipulation by combining a diffusion-based policy with an action-conditioned world model to enable online planning and optimization, consistently outperforming standard behavior cloning across diverse simulation and real-world tasks.
This paper theoretically demonstrates that in distributed computing systems with probabilistic stragglers, the approximation errors of Berrut Approximate Coded Computing (BACC) and Learning Theoretic Coded Computing (LeTCC) schemes converge to zero at specific rates despite the average number of stragglers scaling with the total server count, a finding validated through experiments on various functions including deep neural networks.
This paper introduces GRADIEND, a novel encoder-decoder method that utilizes model gradients to identify and rewrite specific neural network weights, effectively debiasing AI systems regarding gender, race, and religion while preserving their other capabilities.
This paper evaluates the security and quality of code generated by Large Language Models across multiple programming languages using a 200-task dataset, revealing that while LLMs can automate coding, they often fail to adopt modern security features and rely on outdated practices, particularly in languages like C++ and Java 17.
This paper proposes a novel local search algorithm with a balanced optimization objective and provable linear convergence to efficiently identify polarized communities in signed networks, effectively addressing size imbalance and scaling to large datasets while outperforming state-of-the-art methods.
This paper introduces CLEF, a controllable sequence editing framework that learns temporal concepts to precisely target the timing and scope of interventions in longitudinal data, significantly outperforming state-of-the-art baselines in generating accurate and realistic counterfactual trajectories for biological and clinical applications.
This paper demonstrates that integrating Low-Rank Adaptation (LoRA) into Federated Learning for Large Language Models significantly reduces unintended memorization of sensitive training data across diverse model sizes and domains, while maintaining performance and offering compatibility with other privacy-preserving techniques.