9805 papers
This paper proposes that memory consolidation serves a computational role beyond mere stabilization, utilizing "predictive forgetting" to compress stored representations into a form that optimizes generalization by selectively retaining information that predicts future outcomes, a process necessitated by high-capacity encoding constraints and validated through simulations across diverse neural and transformer models.
This paper addresses the computational challenges of generalizing fair top- selection to multiple protected groups while minimizing disparity from a reference function, revealing new hardness barriers for small and proposing an efficient, robust two-pronged solution that incorporates utility loss as an alternative disparity measure.
This paper introduces TREDBench and an embedding-guided synthetic data curation method to adapt the TabPFN 2.5 foundation model for engineering regression tasks, achieving superior predictive accuracy and data efficiency without requiring training on real-world engineering samples.
This paper demonstrates that increasing depth in matrix completion models intensifies coupled dynamics, which drives convergence to low-rank solutions and prevents the loss of plasticity observed in shallow networks.
This paper demonstrates that applying the SAM-Audio speech enhancement model as a preprocessing step for zero-shot ASR with Whisper consistently degrades recognition accuracy despite improving perceptual audio quality, revealing a fundamental mismatch between human-perceived signal cleanliness and machine recognition robustness.
This paper introduces "Probabilistic Dreaming," a novel enhancement to the Dreamer world model that utilizes probabilistic methods to enable parallel latent exploration and maintain distinct hypotheses for mutually exclusive futures, resulting in a 4.5% performance improvement and 28% variance reduction on the MPE SimpleTag domain.
This paper proposes a hybrid methodology combining theoretical modeling with empirical benchmarking to accurately determine the optimal allocation of Prefill-Decode disaggregated hardware resources for Large Language Model inference while satisfying throughput, SLO, and request characteristic constraints.
This paper presents a systematic benchmark study evaluating the effectiveness of pruning, quantization, and knowledge distillation in compressing neural networks for hyperspectral image classification, demonstrating that these methods can significantly reduce model size and computational costs while maintaining competitive accuracy for resource-constrained remote sensing applications.
This paper introduces "Model Medicine," a comprehensive clinical research program that treats AI models as biological-like organisms by establishing a taxonomy of subdisciplines, a behavioral genetics framework, and novel diagnostic tools like Neural MRI to systematically understand, diagnose, and treat model disorders.
This paper introduces Count Bridges, a novel stochastic bridge process for integer-valued data that enables principled generative modeling and the deconvolution of aggregated biological count measurements, such as bulk RNA-seq and spatial transcriptomics, into single-cell resolution profiles.
This paper identifies that pretraining priors undermine the effectiveness of Unlearnable Examples by enabling models to bypass their protective perturbations, and proposes BAIT, a bi-level optimization method that binds perturbations to incorrect targets to override these priors and ensure robust data unlearnability.
This paper introduces Distribution-Conditioned Transport (DCT), a flexible framework that conditions transport maps on learned distribution embeddings to enable generalization to unseen source-target pairs and semi-supervised learning, demonstrating significant performance improvements across synthetic benchmarks and diverse biological applications.
This paper proposes "Interactive Benchmarks," a unified evaluation paradigm that assesses model intelligence through active information acquisition and reasoning under budget constraints in interactive proofs and games, demonstrating that current models still have significant room for improvement in these dynamic scenarios.
This paper introduces CONE, a hybrid transformer encoder that utilizes a novel composite embedding algorithm to preserve the semantics of units and variables for complex numerical data, achieving state-of-the-art performance in numerical reasoning tasks across diverse domains.
KindSleep is a deep learning framework that integrates clinical knowledge with single-channel oximetry and patient data to accurately diagnose obstructive sleep apnea, achieving superior performance and enhanced transparency across large, diverse datasets.
This landscape commentary evaluates the GPT-5 family against GPT-4o, revealing substantial improvements in expert-level textual reasoning and multimodal synthesis that approach state-of-the-art performance in tasks like mammography, while highlighting that generalist models still lag behind specialized systems in perception-critical domains such as neuroradiology.
This paper introduces ConTSG-Bench, a unified benchmark featuring a large-scale, multi-modal dataset and comprehensive metrics to systematically evaluate and analyze the performance, limitations, and future directions of conditional time series generation models.
This paper proposes a distributional risk-sensitive reinforcement learning framework that integrates Information Bottleneck representations and Conditional Value-at-Risk optimization to achieve certified worst-case DRAM equalizer performance with significant speedups and uncertainty quantification, outperforming existing methods by up to 89.1% on real-world memory data.
This paper presents the first structural-assumption-free causal discovery method for linear non-Gaussian latent-variable cyclic models by establishing a graphical criterion for distributional equivalence, introducing edge rank constraints, and providing an algorithm to recover models up to this equivalence class.
This paper introduces a novel and efficient on-policy reinforcement learning method that trains diffusion policies by aligning policy iteration with the diffusion process, thereby overcoming computational bottlenecks in log-likelihood estimation while enabling multimodal behavior generation and entropy regularization across diverse benchmark tasks.