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2,173 papers
This work proposes neural network nudging, a data-driven method for learning nudging terms in nonlinear state space models and establishes a theoretical existence result based on the Kazantzis--Kravaris--Luenberger observer theory.
This paper introduces, for the first time, exact constrained reformulations for direct metric optimization (DMO) problems, which can be effectively solved by exact penalty methods and is expected to be applicable to a wide range of DMO problems for binary IC and beyond.
PEM-UDE, a method that combines prediction-error methodology with universal differential equations to discover governing equations from limited, noise-corrupted observations, yields a multi-scale neural mass model that ties single-neuron parameters to macroscopic network dynamics and predicts a relationship between connection density, dominant oscillation frequency, and synchrony.
The Continuous Evolution Pool (CEP), a replay-free framework that maintains a dynamic pool of specialized forecasters, is proposed, which employs a retrieval mechanism to identify the nearest concept based on gene similarity, an evolution strategy to spawn new forecasters upon detecting distribution shifts, and an elimination policy to prune obsolete models under memory constraints.
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This article presents the first study on the lowest cost required to find a monotone classifier whose error is at most $(1 + \epsilon) \cdot k^*$ where $\epsilon \ge 0$ and $k^*$ is the minimum error achieved by an optimal monotone classifier.
This is the first global convergence and recovery result for EM or Gradient EM beyond the special case of m=2, and it is proved that with only mild over-parameterization, randomly initialized gradient EM converges to the ground truth with polynomial time and samples.
A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.
The LZ penalty is introduced, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of capability and without instances of degenerate repetition, and enables state-of-the-art open-source reasoning models to operate with greedy decoding without loss of capability and without instances of degenerate repetition.
This study provides a theoretical analysis showing that gradient heterogeneity, together with Hessian heterogeneity, degrades the convergence of gradient-based methods such as SGD, while sign-based methods are substantially less sensitive to this effect.
Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.
Comprehensive experiments on four molecular benchmarks, including the four large-scale Open Graph Benchmark datasets, substantiate the effectiveness of hyperbolic positional encodings in enhancing the performance of Graph Transformers and provide extensive theoretical underpinnings to offer insights into the working mechanism of the HyPE framework.
A comprehensive re-evaluation of two memory-based methods for self-improving agents is conducted, broadening the scope of evaluation along two axes and hypothesizing that task and environment underspecification contribute to this fragility.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
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