11765 papers
The paper introduces NEURONA, a neuro-symbolic framework that integrates symbolic reasoning with fMRI grounding to decode interacting visual concepts, demonstrating that incorporating structural priors significantly enhances both decoding accuracy and generalization to unseen queries.
GreenPhase is an efficient, interpretable, and sustainable deep-learning model based on the Green Learning framework that achieves state-of-the-art earthquake detection and phase picking performance on the STEAD dataset while reducing computational costs by approximately 83% through its unique feed-forward, multi-resolution architecture that eliminates backpropagation.
This paper introduces SMMA, a fully automated deep learning framework that accurately measures geniohyoid muscle thickness during speech, enabling scalable analysis of speech motor control and objective assessment of related disorders by eliminating the need for time-consuming manual annotation.
This paper corrects mathematical errors in the original theoretical foundation of the UMAP algorithm, provides a self-contained derivation of the underlying functors and metric realization, and clarifies the correspondence between the theoretical framework and the practical UMAP implementation.
This paper proposes a "Learning Order Forest" method that employs a joint learning mechanism to iteratively construct tree-based distance structures for qualitative attributes, thereby effectively capturing local order relationships to achieve superior clustering performance on datasets with nominal values.
This paper introduces GLOT, a lightweight and efficient structure-aware pooling module that constructs and refines token-similarity graphs from frozen LLM outputs to achieve robust sentence representations with significantly fewer parameters and faster training times compared to existing methods.
This paper proposes a novel, implementable adaptive parameter selection strategy for kernel-based gradient descent that integrates bias-variance analysis with the splitting method and empirical effective dimension to achieve optimal generalization error bounds across diverse kernels, target functions, and error metrics.
This paper introduces heterogeneous time steps (HTS) to Equilibrium Propagation, demonstrating that assigning neuron-specific time constants drawn from biologically motivated distributions improves training stability and robustness while maintaining competitive performance.
This paper introduces the Surprisal-Rényi Free Energy (SRFE), a novel log-moment-based functional that bridges forward and reverse Kullback-Leibler divergences by revealing a mean-variance tradeoff and providing a variational characterization that controls large deviations in code-length, thereby clarifying the geometric and statistical structure underlying these distinct learning objectives.
This paper introduces a simple variant of the Squint algorithm for the classic expert problem and proves, via a straightforward modification of the original proof, that it achieves a regret bound similar to that of a recent variant of the NormalHedge algorithm.
This paper proposes a scalable, contrastive causal discovery model that leverages paired observational and single-regime soft interventional data to construct globally consistent causal structures, theoretically proving its ability to recover identifiable edges and outperform non-contrastive methods in both in-distribution and out-of-distribution scenarios.
This replication study of FairDICE, a multi-objective offline reinforcement learning algorithm, reveals that while its theoretical claims hold, a critical code error initially reduced it to standard behavior cloning and underspecified hyperparameters hindered reproducibility, though corrected experiments demonstrate its potential to scale to complex environments despite a reliance on online tuning.
This paper demonstrates that a significant portion of transformer MLP nonlinearity is redundant and context-dependent, showing that a lightweight gating mechanism can dynamically replace these computations with linear surrogates to reduce computational waste or, when applied strategically with full retraining, actively improve model performance by eliminating harmful nonlinearities.
This paper introduces Graph Hopfield Networks, an energy-based framework that unifies associative memory retrieval with graph Laplacian smoothing to achieve state-of-the-art node classification performance and enhanced robustness across diverse graph benchmarks.
This paper challenges the conventional practice of stopping diffusion model training at the minimum test loss by identifying a "biased generalization" phase where models continue to lower loss while overfitting to training data, a phenomenon driven by the sequential nature of feature learning that poses risks for privacy-critical applications.
This paper reveals that state-of-the-art mathematical reasoning models often achieve high benchmark accuracy through computationally unstable and unfaithful pathways, masking significant rates of silent failures and demonstrating that increased model scale does not necessarily improve reliability or correctness.
This paper proposes a minimax optimal algorithm for tabular reinforcement learning with delayed state observations that combines augmentation and upper confidence bound methods to achieve a regret bound of , which is proven to be optimal up to logarithmic factors.
The paper introduces PERSIST, a novel world model paradigm that simulates the evolution of a latent 3D scene to overcome the spatial memory and consistency limitations of existing video generation methods, thereby enabling coherent, long-horizon interactive experiences with persistent 3D state and geometry-aware control.
This paper introduces MasCOR, a machine-learning-assisted framework that overcomes the computational limitations of traditional mathematical programming to enable rapid, near-optimal co-optimization of e-fuel system design and real-time operation under renewable uncertainty, demonstrating site-specific strategies for cost-effective carbon-neutral methanol production across diverse European locations.
This paper addresses the critical reliability challenges in Compute-in-Memory neural accelerators caused by device non-idealities by demonstrating the disproportionate impact of small variations on safety-critical workloads and proposing cross-layer solutions, including a selective write-verify mechanism (SWIM) and noise-aware training, to ensure robust and efficient deployment.