4460 papers
This paper proposes SPDIM, a parameter-efficient geometric deep learning framework that leverages information maximization on the symmetric positive definite (SPD) manifold to effectively address source-free unsupervised domain adaptation in EEG data, specifically overcoming the generalization limitations of prior methods when faced with label shifts.
This paper proposes an unsupervised prognostics framework that utilizes unlabeled run-to-failure data to simultaneously identify latent failure modes and select informative sensors, thereby enabling accurate remaining useful life prediction for autonomous deep-space habitats under multiple unknown failure conditions.
This paper introduces MS-HGNN, a morphological-symmetry-equivariant heterogeneous graph neural network that integrates robotic kinematic structures and symmetries as architectural constraints to achieve high generalizability and efficiency in learning dynamics for various multi-body systems, with its effectiveness validated through formal proofs and experiments on quadruped robots.
This paper introduces CuriousBot, a mobile exploration system that utilizes a 3D relational object graph to enable active interaction with diverse objects in complex environments, outperforming vision-language model-based approaches in both effectiveness and generalization.
This paper demonstrates that simple, local molecular fingerprints combined with LightGBM outperform complex graph neural networks and transformers across 132 peptide datasets, challenging the assumption that modeling long-range interactions is essential for accurate peptide function prediction.
This paper introduces Astromer 2, an enhanced foundational model for light curve analysis that leverages self-supervised pre-training on 1.5 million MACHO survey observations and weighted per-sample embeddings to significantly outperform its predecessor and prior models in classification tasks, particularly when trained with limited labeled data.
This paper proposes a unified framework to evaluate how personalization affects both prediction accuracy and explanation clarity in high-stakes domains, deriving statistical bounds to determine when such effects are detectable given specific dataset characteristics.
This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.
This paper presents an end-to-end Real2Sim2Real framework for deformable linear object manipulation that employs likelihood-free inference to estimate physical parameter distributions for domain-randomized reinforcement learning, enabling zero-shot deployment of visuomotor policies from simulation to the real world.
This paper introduces Feature Importance Rescaling (FIR), a theoretically grounded method that improves the reliability of cluster validity indices in noisy, high-dimensional Gaussian mixtures by attenuating irrelevant features, thereby strengthening the correlation between unsupervised evaluation metrics and ground truth.
This perspective proposes the concept of "functional units" as a critical bridge to reconcile traditional structure-property correlations with emerging data-driven AI paradigms, thereby advancing the understanding of material design and knowledge inheritance across diverse systems.
The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.
This paper presents mixed-integer linear programming formulations to construct globally optimal model trees with linear support vector machines at the leaves, demonstrating through extensive experiments that they achieve competitive accuracy with significantly smaller, more interpretable structures compared to greedy model trees and other standard machine learning algorithms.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.
This paper introduces CDGLT, a training-efficient framework for multimodal metaphor identification that leverages Concept Drift via Spherical Linear Interpolation and adapted LayerNorm tuning to achieve state-of-the-art performance on the MET-Meme benchmark while significantly reducing computational costs compared to existing generative methods.
This paper introduces Stepwise Guided Policy Optimization (SGPO), a framework that enhances Group Relative Policy Optimization (GRPO) by utilizing a step-wise judge model to provide learning signals from all-negative sample groups, thereby enabling large language models to learn from incorrect reasoning and improving performance across various reasoning benchmarks.
This paper introduces the Gaussian-Multinoulli Restricted Boltzmann Machine (GM-RBM), a generative model that extends the standard GB-RBM by employing q-state Potts hidden units to better capture discrete, structured representations, demonstrating competitive performance on analogical recall and memory benchmarks while offering a scalable alternative to binary latent models.
The paper introduces JULI, a black-box jailbreaking technique that manipulates top-5 token log probabilities via a lightweight plug-in called BiasNet to effectively bypass safety alignment in API-accessible Large Language Models without requiring access to model weights or the generation process.
This paper introduces a novel framework for discovering symbolic differential equations from data by utilizing symmetry invariants as atomic building blocks, thereby ensuring that the resulting equations inherently respect physical laws while improving the accuracy and efficiency of existing discovery methods.
The paper introduces UltraEdit, a training-, subject-, and memory-free approach for lifelong language model editing that achieves unprecedented scalability and efficiency by computing parameter shifts in a single step, enabling 7B models to be edited on consumer GPUs with over 2 million updates while outperforming existing methods in speed, memory usage, and accuracy.