11557 papers
This paper proposes a novel context biasing method for automatic speech recognition that leverages user-provided on-the-fly corrections of substitution errors to effectively resolve pronunciation-orthography mismatches, achieving a 22% to 34% relative improvement in biased word error rates without compromising overall system performance.
Meta FAIR introduces UMA, a family of universal models for atoms trained on half a billion 3D structures using a novel mixture of linear experts architecture, which achieves state-of-the-art speed and accuracy across diverse chemical domains without requiring fine-tuning.
The paper introduces UQLM, a Python package that leverages state-of-the-art uncertainty quantification techniques to generate confidence scores for detecting hallucinations and enhancing the reliability of Large Language Model outputs.
This paper proposes Q-SVMPC, a novel framework that integrates Q-guided Stein variational inference with an RL-informed policy prior to enable diverse, robust, and sample-efficient trajectory optimization in Model Predictive Control, overcoming the mode collapse limitations of existing learning-based MPC methods.
This paper introduces Fast Equivariant Imaging (FEI), a novel unsupervised learning framework that leverages the Augmented Lagrangian method and auxiliary Plug-and-Play denoisers to achieve a 10x training acceleration and improved generalization for deep imaging tasks like X-ray CT reconstruction and inpainting without requiring ground-truth data.
This paper introduces a probabilistic early-exit framework for single-channel speech separation and enhancement that dynamically scales computational resources based on uncertainty-aware signal quality estimates, enabling efficient deployment on heterogeneous devices without compromising reconstruction performance.
This paper employs interpretability techniques on the off-by-one addition task to reveal that large language models achieve task-level generalization through a reusable "function induction" mechanism, where multiple attention heads collaboratively learn and compose abstract functions to solve unseen problems.
This paper establishes Gaussian approximation bounds for the finite-dimensional distributions of deep neural networks with randomly initialized weights and Lipschitz activations, proving convergence to a Gaussian limit as layer widths grow and deriving specific convergence rates that depend on the network depth.
This paper introduces "Poker," a new self-supervised Inductive Logic Programming system that learns recursive logic programs from positive examples and a general second-order background theory by automatically generating and labeling synthetic negative examples, thereby overcoming the need for expert-curated negative data and task-specific background theories that limit existing methods like Louise.
This paper proposes a dependence-aware evaluation methodology for Temporal Convolutional Networks that controls for effective sample size rather than raw sequence length, providing new generalization bounds and demonstrating that stronger temporal dependence can paradoxically reduce generalization gaps when properly accounted for.
This paper introduces ObfusQAte and the ObfusQA framework, a novel benchmark designed to evaluate Large Language Model robustness by testing their performance on factual questions subjected to multi-tiered obfuscation, revealing that models often fail or hallucinate when faced with nuanced linguistic variations.
This paper introduces Subsampling Factorization Machine Annealing (SFMA), an enhanced optimization algorithm that utilizes probabilistic training on subsampled datasets to achieve a superior balance between exploration and exploitation, thereby outperforming standard Factorization Machine Annealing in speed, accuracy, and scalability for large-scale black-box optimization problems.
This paper demonstrates through numerical experiments and analytical derivation that Quantum Generative Adversarial Networks (QGANs) with pure-state generators fail to generalize beyond the average training data representation, a limitation theoretically explained by a fidelity-based lower bound on discriminator quality.
This paper introduces zono-conformal prediction, a data-efficient method that utilizes zonotopes to generate less conservative, statistically valid uncertainty sets for both regression and classification tasks by directly embedding these sets into the base predictor via a single linear program.
This foundational concept paper proposes the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), an architecture integrating quantized neural networks and adaptive multi-sensor fusion to enable real-time, high-fidelity crater detection on resource-constrained, radiation-hardened space exploration hardware.
This paper discusses the limitations of current quantitative benchmarks for evaluating Generative AI in healthcare and advocates for comprehensive assessment strategies that incorporate clinical context, human expertise, and cost-effective computational models to ensure generalizability in real-world medical environments.
QDFlow is an open-source Python package that simulates realistic quantum dot device data with ground-truth labels by combining a self-consistent Thomas-Fermi solver, dynamic capacitance modeling, and customizable noise modules to address the scarcity of experimental datasets needed for machine learning applications.
This paper addresses the unexplored challenge of label noise in action-based video object segmentation by introducing the ActiSeg-NL benchmark, analyzing the impact of textual and mask annotation noise, and proposing a Parallel Mask Head Mechanism to enhance robustness for embodied intelligence applications.
This paper introduces Nested Subspace Networks (NSNs), a novel architectural paradigm that re-parameterizes linear layers to enable a single large language model to be dynamically adjusted across a continuous spectrum of compute budgets at inference time, achieving a smooth and predictable trade-off between efficiency and performance without requiring retraining or multiple specialist models.
This paper introduces AxelGNN, a novel Graph Neural Network architecture inspired by Axelrod's cultural dissemination model that utilizes similarity-gated interactions, segment-wise feature copying, and global polarization to effectively address oversmoothing and heterophily challenges while achieving competitive performance across diverse graph types.