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3,822 papers
Activation-Keyed Momentum (AK-Momentum), which builds direction-awareness into the momentum update rule, is proposed, and it is proved that it is a valid momentum, that it applies the input-side curvature correction without matrix inversion, and that it clears stale directions faster than EMA under both a fixed and a d...
SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss, jointly optimizes accuracy, efficiency, and validity without batch-splitting or a predefined confidence level during training.
This work proposes that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player, which yields a provably convergent active data selection algorithm.
Distinguishing profile-shape equivalence from fixed-parameter operator equivalence separates new normalization shapes from adaptive rescalings and organizes the operators by support, tail behavior, and realization complexity.
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This paper extends the cooperative multi-task SemCom framework to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset and adopts the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables.
It is shown that the critical scaling and asymptotic limit are uniquely determined by the decay of correlations together with the Hermite rank of the feature function, which represents meaningful hyperparameters in the asymptotic regime.
This paper derives oracle risk bounds for learning dense and sparse routing with evolving experts, separating approximation, expert-learning, and router-estimation errors, and characterize how sparse Top-K routing can retain the benefits of localized aggregation while controlling per-input computation.
This work proposes Adaptive Local Relational Alignment (ALRA), a position-specific framework combining student proposals with teacher guidance, which replaces the teacher-mass coefficients of the two conditional terms with unit coefficients, preventing either term from being downweighted solely because its region has l...
A high-probability uniform ($L^{\infty}$) recovery guarantee that jointly controls approximation and statistical errors while enjoying an optimization error of zero is introduced.
Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity and establishes a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_ p(n^{-1/2})$.
The Conformal Relevance framework is introduced which uses in-context learning example curation and ensembling to create a score function which maintains coverage while improving conciseness with minimal manual input.
A statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space and consistently improves detection performance is proposed, showcasing a promising approach for merging classical network analysis with deep learning.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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