Home Knowledge Base the Input-Output

the Input-Output

No mentions found

This entity hasn't been tracked yet, or Iris is still building its knowledge base.

Related Articles from SNS

NI Analytical Input-Output tables 2022

NI Analytical Input-Output tables 2022 NI Analytical Input-Output tables 2022 – industry by industry Applies to Northern Ireland Documents Details The Supply-Use Tables are the starting point for the production of the Input-Output Analytical Tables. For the analysis of industry linkages and economic impacts, it is more meaningful to represent the Use Table in Industry by Industry (IxI) form.

GOV.UK Statistics 7d ago

State-Space Neural Network with Ordered Variance for Model Order Determination

arXiv:2406.10359v3 Announce Type: replace Abstract: This paper addresses the problem of identifying a nonlinear state-space model, along with an adequate model order, from a given input-output training dataset. To this end, a novel framework, termed state-space neural network with ordered variance (SSNNO), is proposed. In SSNNO, the state variables are ordered according to their variances computed using the training data.

arXiv CS 8d ago

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise

arXiv:2605.18528v2 Announce Type: replace-cross Abstract: A growing lesson from neural network optimization is that optimizer design should respect how the model is parametrized. Scale-invariant methods become important because their normalized layerwise updates can not only support hyperparameter transfer across model sizes but exploit input-output matrix norm geometry. At the same time, stochastic gradient noises in deep learning are often far from sub-Gaussian and may exhibit heavy tails.

arXiv CS 8d ago

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606.07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences. Recent Prior-Data Fitted Networks (PFNs) suggest that synthetic tasks can be useful for learning transferable inference behavior. However, directly transferring this paradigm to time-series forecasting remains difficult, since temporal order, dynamic lags, and recurring historical...

arXiv CS 2d ago

Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning

Announce Type: new Abstract: End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous robotic manipulation. However, they inherit two key limitations from 2D foundation models: 1) the reliance on 2D RGB inputs that ignores the intrinsically 3D nature of manipulation; and 2) the lack of spatial 3D alignment between input-output spaces as well as across diverse robot embodiments, camera setups, and...

arXiv CS 8d ago

Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning

Announce Type: replace Abstract: End-to-end manipulation policies, combined with web-scale pretrained Vision-Language Models (VLMs), show the promise for generalizable and dexterous robotic manipulation. However, they inherit two key limitations from 2D foundation models: 1) the reliance on 2D RGB inputs that ignores the intrinsically 3D nature of manipulation; and 2) the lack of spatial 3D alignment between input-output spaces as well as across diverse robot embodiments, camera setups, and...

arXiv CS 1d ago

Data-Enabled Predictive Control with Predictive Adaptive Line-of-Sight Guidance for 3-D Path Following of Autonomous Underwater Vehicles

arXiv:2510.25309v3 Announce Type: replace Abstract: This paper presents a fully data-driven 3-D path-following framework for autonomous underwater vehicles (AUVs), a representative class of underwater field robotics, based on Data-Enabled Predictive Control (DeePC). The approach eliminates explicit hydrodynamic modeling by exploiting measured input-output trajectories to predict and optimize future system behavior. Classic DeePC is employed for heading control, while a cascaded DeePC...

arXiv CS 8d ago

Linear Ordering Problem: Time for a Change

arXiv:2605.31051v1 Announce Type: new Abstract: The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social choice, and machine learning. Its most prominent use is the triangulation of economic input-output tables, which helps identify critical industries in an economy.

arXiv CS 9d ago

Unstable Poles Arising in AC Power Grid Subsystem Representations

new Abstract: Recent small-signal stability studies of AC grids have shifted towards analysing power systems as interconnections of subsystems and leveraging their input-output properties to derive scalable stability certificates. Two subsystem representations appear frequently in the literature: the PQ model, coupling powers to phase angle and voltage magnitude, and the IV model, coupling currents to voltages. In this paper, we derive both models without simplifying the bus or line dynamics...

arXiv CS 7d ago

LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification

arXiv:2412.12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods. Addressing the intricate challenges of data-driven discovery in nonlinear dynamical systems, these methods have garnered significant attention. Among them, Sparse Identification of Nonlinear Dynamics (SINDy) has emerged as a transformative approach,...

arXiv CS 8d ago