4124 papers
This paper proves that randomly initialized, polynomially over-parameterized convolutional neural networks contain structured subnetworks capable of approximating smaller networks without training, by developing new mathematical tools to overcome previous limitations in analyzing the Strong Lottery Ticket Hypothesis for structured pruning.
This paper proposes a novel, theoretically grounded graph clustering method that constructs homophilic and heterophilic graphs to build low-pass and high-pass filters enhanced by a squeeze-and-excitation block, effectively addressing the limitations of existing approaches in handling the structural disparities of real-world graphs.
This paper proposes a deep learning framework that jointly discovers optimal coordinates and flow maps to enable precise, computationally efficient time-stepping for multiscale systems, achieving state-of-the-art predictive accuracy with reduced costs on complex models like the Fitzhugh-Nagumo neuron and Kuramoto-Sivashinsky equations.
This paper proposes a fairness-aware multi-group target detection approach for online discussions that effectively reduces bias across demographic groups while maintaining strong predictive performance, particularly in the context of toxicity detection where harm is highly dependent on the targeted group.
This paper proposes and evaluates a distributed multi-agent Q-learning solution for HD map updates in vehicular networks that reduces computational burdens and compatibility issues while significantly improving time latencies across various traffic scenarios compared to single-agent approaches.
This paper introduces Sparse Variational Student-t Processes (SVTP), a scalable framework that extends sparse inducing point methods to Student-t processes via novel inference algorithms and natural gradient optimization, achieving superior robustness to outliers and heavy-tailed data with significantly faster convergence and lower prediction error compared to sparse Gaussian processes on large datasets.
This paper introduces HYGENE, the first deep learning-based diffusion method that generates realistic and diverse hypergraphs by iteratively expanding a bipartite representation from a single connected node pair through a progressive local expansion process.
This paper introduces a unified framework that models quantization and sparsification as additive noise to derive a principled, noise-corrective gradient path, enabling the stable training of neural networks at arbitrary low precisions and sparsity levels without relying on heuristic estimators like the Straight-Through Estimator.
The paper introduces ARLBench, a flexible and efficient benchmark for hyperparameter optimization in reinforcement learning that utilizes a representative subset of tasks to enable cost-effective comparisons of diverse AutoRL methods and lower the barrier to entry for researchers with limited compute resources.
The paper introduces DRUPI (Dataset Condensation using Privileged Information), a framework that enhances dataset condensation by synthesizing auxiliary privileged information, such as feature or attention labels, alongside reduced data to significantly improve model training performance across various benchmarks.
This paper proposes a novel fairness-aware data repair framework that utilizes a Bayesian nonparametric stopping rule to learn robust optimal transport operators, thereby overcoming representation bias in underrepresented subgroups and enabling effective repairs on out-of-sample archival data while balancing fairness against data distortion.
This paper proposes an unsupervised representation learning framework that factorizes latent variable transformations into sparse rotational and potential flow fields, enabling the model to learn disentangled representations based on independent transformation primitives while achieving state-of-the-art performance in data likelihood and equivariance on sequence data.
This paper proposes a data-driven framework combining control barrier functions and differentiable optimization to learn interpretable responsibility allocations, enabling autonomous agents to quantify and adjust their behaviors for safe, socially-aware multi-agent interactions.
This paper proposes a novel machine learning framework that combines gradient descent on the Grassmannian manifold with Donaldson's algorithm to efficiently compute Ricci-flat approximations of Calabi-Yau metrics, demonstrating its effectiveness and the emergence of nontrivial local minima on the Dwork family of threefolds.
This paper introduces Adaptive Importance Sampling and Stratified Subsampling estimators that achieve minimax-optimal rates for robust high-dimensional sparse regression under heavy-tailed noise, contamination, and temporal dependence, while also providing fully specified de-biasing procedures for valid confidence intervals and demonstrating superior empirical performance over uniform subsampling.
The paper introduces Scalable Message Passing Neural Networks (SMPNNs), a deep Graph Neural Network architecture that replaces computationally expensive attention mechanisms with standard convolutional message passing within a Pre-Layer Normalization Transformer-style block, achieving state-of-the-art performance on large graphs while theoretically addressing oversmoothing through the necessity of residual connections for universal approximation.
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.