Model Complexity
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Related Articles from SNS
Hepatic Differentiation of Human Pluripotent Stem Cells into Functional In Vitro Models Recapitulating Native Liver Complexity for MASLD Modelling
Human in vitro hepatic models that accurately recapitulate liver function are essential for fundamental and translational research; however, currently utilised models for disease modelling and drug discovery lack physiological fidelity and require prolonged culture time. Here, we present a streamlined 10-day protocol for efficient and reproducible differentiation of human pluripotent stem cells into hepatocyte like cells (HLCs) and hepatic liver organoids (HLOs). Both models exhibited mature...
An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
arXiv:2606.04409v1 Announce Type: new Abstract: Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision. However, the source of their generalization performance remains difficult to explain using traditional statistical learning theory. Among the factors that may affect visual generalization, data scale, model complexity, and input modalities are fundamental and controllable variables.
An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
arXiv:2606.04409v2 Announce Type: replace Abstract: Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision. However, the source of their generalization performance remains difficult to explain using traditional statistical learning theory. Among the factors that may affect visual generalization, data scale, model complexity, and input modalities are fundamental and controllable variables.
Partisan voter model on complex networks: Dynamics of local ordering
arXiv:2606.05062v1 Announce Type: new Abstract: We investigate the processes of local ordering for the partisan voter model on complex networks. In this model agents hold a binary opinion and a fixed preference that biases updates toward alignment with their preferred state. We first study the dynamics on uncorrelated random networks and derive a pair approximation that resolves the densities of links between different classes of agents.
Compatibility and Accuracy Verification of CADmesh-Based Complex Geometry Modeling in Geant4
arXiv:2606.06508v1 Announce Type: new Abstract: Geant4 Monte Carlo simulation relies on the Constructive Solid Geometry (CSG) method for complex geometric modeling. This method has low efficiency and a high application threshold. Importing triangular facet formats such as STL/OBJ via CADmesh is a promising alternative, but systematic evaluations of format compatibility, geometric accuracy, and physical simulation deviations are lacking.
CoVEBench: Can Video Editing Models Handle Complex Instructions?
Announce Type: new Abstract: While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt often demands multiple coupled edits, such as modifying subjects, actions, and camera views, while strictly preserving unrelated spatiotemporal content. Existing benchmarks, heavily constrained by isolated edits and coarse global metrics, fail to diagnose how models handle such complex...
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
Announce Type: cross Abstract: Modern Machine Learning (ML) and Artificial Intelligence (AI) models, especially large language models (LLMs), are increasingly used to generate scientific hypotheses and mechanistic explanations from observational data. This position paper argues that in the high-dimensional proxy regimes where modern ML excels, mechanistic learning is generically underdetermined: many incompatible mechanisms induce essentially the same observational relationships on the...
Complex-phase stochastic modeling of mitochondrial heteroplasmy
Mitochondrial heteroplasmy the coexistence of both wild-type and mutant copies of mitochondrial DNA ( mtDNA ) within a cell is a key factor in the pathogenesis of mitochondrial diseases. Classical approaches, which rely solely on the scalar fraction of mutant DNA, fail to fully account for threshold effects, the stochastic nature of heteroplasmy dynamics , and tissue specificity. The aim of the work is to construct a complex stochastic model of heteroplasmy dynamics , which for the first...
Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks
Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining. We argue that inductive cross-network generalisation is a necessary prerequisite for Graph Foundation Models (GFMs) in this domain and propose four design properties towards this goal. As a proof of concept, ts-net...
Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
Announce Type: replace Abstract: Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining. In this work, we study the minimal parameter budget required for implicit reasoning, defined as the ability to infer new facts from learned knowledge without explicit chain-of-thought supervision. To isolate this phenomenon, we pretrain LMs from scratch in a controlled synthetic environment that mimics...