Deep learning adapts HVAC models to new buildings.
problem Adapting thermal dynamics models to new buildings with limited data.
method Deep supervised domain adaptation (DSDA) using LSTM-based Sequence to Sequence model.
result Deep supervised domain adaptation improves predictive performance over learning from scratch.
Deep RL improves space heating control with faster computation and robustness.
problem Suboptimal performance and inability to adapt to dynamic conditions in classical heating control methods.
method Deep reinforcement learning algorithm for optimal control of space heating systems.
result Outperforms rule-based control by 5-10% in simulated environments.
Paper models buildings' thermal characteristics with a Bayesian approach.
problem Modeling buildings' heat dynamics with various factors.
method Bayesian state-space model incorporating prior knowledge.
result Bayesian approach provides similar parameters as MCMC but faster.
Deep learning networks are approximated using dynamical systems theory.
problem Understanding the approximation capabilities of deep learning networks.
method Modeling deep residual networks as continuous-time dynamical systems and using approximation theories in Lp. result Established general sufficient conditions for universal approximation of deep residual networks.
Develops parameter-free online mirror descent for optimal dynamic regret.
problem Optimal online linear optimization in unbounded domains.
method Modified online mirror descent framework for parameter-free algorithms.
result First unconstrained online linear optimization achieving optimal dynamic regret.
The paper tackles energy management in buildings with PCM using dynamic programming.
problem Optimal scheduling of HVAC systems in buildings with PCM is challenging due to nonlinear and non-convex characteristics.
method The paper uses dynamic programming to address the nonlinear nature of PCM, incorporating macro actions and multi-time scale Markov decision processes to reduce computational burden.
result The proposed method demonstrates a computational speed-up of up to 12,900 times compared to direct DP application.
The paper proposes a method to improve reinforcement learning by ensuring consistency between observed and imagined dynamics.
problem Compounding errors in traditional model-based reinforcement learning approaches.
method An auxiliary cost function to ensure consistency between observed and imagined dynamics.
result The proposed approach helps train powerful policies and better dynamics models.
Using Caputo fractional derivative of order α we build the fractional jet bundle of order α and its main geometrical structures. Defined on that bundle, some fractional dynamical systems with applications to economics are studied.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Review of algorithms for linear system approximations.
problem Linear approximation of high-dimensional dynamical systems.
method State-of-the-art algorithms for low-rank DMD.
result Provides additional details for comprehensive understanding.
Mathematical study of learning long-term integration in linear RNNs.
problem How do linear recurrent neural networks learn to integrate over long timescales?
method Analytical study of linear RNNs trained to integrate white noise and damped oscillatory filters.
result Learning dynamics are described by low-dimensional effective equations for outlier eigenvalues.
Improved Granger causality method for dynamic time series data.
problem Traditional Granger causality method assumes constant causalities, failing to model dynamic causalities.
method Dynamic window-level Granger causality (DWGC) method with causality indexing.
result Improved DWGC method better detects window-level causalities.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
We postulates, and then show experimentally, that liquidity deficit is the driving force of the markets. In the first part of the paper a kinematic of liquidity deficit is developed. The calculus-like approach, which is based on Radon--Nikodym derivatives and their generalization, allows us to calculate important chara…
A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built. These include hierarchical models for variances of other variables and many nonlinear models. The underlying variational B…
Model predicts global financial market risks and asset allocation.
problem Predicting downside risk and market regime shifts.
method Dynamic regime switching model based on GARCH-DCC-Copula.
result Significantly improves risk and alpha-based asset allocation strategies.
This research explores the dynamics of linearised neural nets, revealing distinct learning phases and layer growth rates.
problem Understanding the fundamental mechanics of neural nets and their learning dynamics.
method Derivation of properties of learning dynamics in general multi-layer linear neural nets, including orthogonal networks.
result Linear multi-layer neural nets exhibit distinct phases of learning with different layer growth rates, and nonlinearity affects these dynamics.
Novel methods improve Bayesian analysis of chaotic dynamical systems.
problem Bayesian parameter inference and trajectory reconstruction of chaotic systems with sparse and noisy data.
method Pilot MAGI (pMAGI) and Pilot MAGI Sequential Prediction (PMSP) methods.
result pMAGI and PMSP significantly outperform existing methods in accuracy and computational efficiency.
Proposes continuous graph neural networks to capture long-range dependencies.
problem Capturing long-range dependencies in graph data.
method Defines continuous dynamics for graph neural networks using diffusion-based methods.
result Proposed continuous graph neural networks are effective and deeper networks can capture long-range dependencies.
We present about twenty conjectures, problems and questions about flat manifolds. Many of them build the bridges between the flat world and representation theory of the finite groups, hyperbolic geometry and dynamical systems.
In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-sp…
D2PCCA integrates deep learning and probabilistic modeling for nonlinear dynamical systems.
problem Analyzing nonlinear dynamical systems with probabilistic understanding.
method Combines deep learning and probabilistic modeling, using KL annealing and normalizing flows.
result Captures latent dynamics in sequential datasets with improved convergence and flexibility.
Proposes a model for identifying edges in low-rank dynamical networks.
problem Inability of conventional methods to handle low-rank dynamical networks.
method Low rank dynamical network model with causal Wiener filtering.
result Consistent method for estimating all network edges.
dynoNet learns dynamical systems using linear operators.
problem Learning complex dynamical systems.
method dynoNet uses linear dynamical operators for sequence modeling and system identification.
result dynoNet effectively identifies systems on benchmarks.
Considering a Hamiltonian Dynamical System describing the motion of charged particle in a Tokamak or a Stellarator, we build a change of coordinates to reduce its dimension. This change of coordinates is in fact an intricate succession of mappings that are built using Hyperbolic Partial Differential Equations, Differen…
Crochet creates precise 2D shapes from 1D material.
problem Creating precise 2D shapes from 1D material.
method Using crochet to generate constant flat, spherical, or hyperbolic shapes.
result Crochet is the most flexible and precise method for building dynamical systems with high curvature precision.
Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dy…
Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of w…
New model predicts dynamic volatility in uncertain financial markets.
problem Predicting dynamic volatility in financial markets with uncertainty.
method Generalized Barndorff-Nielsen and Shephard (BN-S) model considering delay and fuzziness.
result Effective prediction of dynamic volatility with improved performance.
Robots learn movement libraries by segmenting complex trajectories.
problem Segmenting complex robot movement demonstrations for library building.
method Model trajectories as Switching Linear Dynamical Systems and infer segmentation using a nonparametric Bayesian approach.
result Robots can learn movement libraries more effectively by segmenting demonstrations.
Random Forest proximity measures for multi-view classification.
problem Combining multiple heterogeneous data views for classification.
method Building dissimilarity representations for each view, fusing them dynamically.
result Dynamic View Selection improves multi-view classification performance.
Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clusterin…
Algorithm reconstructs interaction topology in linear dynamical systems.
problem Learning influence pathways in dynamically related processes.
method Physics-informed multivariate Wiener filtering.
result Topology of interactions can be exactly recovered for certain classes.
Using the fractional integration and differentiation on R we build the fractional jet fibre bundle on a differentiable manifold and we emphasize some important geometrical objects. Euler-Lagrange fractional equations are described. Some significant examples from mechanics and economics are presented.
We prove a Morse Lemma for coarsely regular quasigeodesics in nonpositively curved symmetric spaces and euclidean buildings X. The main application is a simpler coarse geometric characterization of Morse subgroups of the isometry groups Isom(X) as undistorted subgroups which are coarsely uniformly regular. We show furt…
The paper optimizes RF training by improving tree building algorithms and CPU optimizations.
problem Improving the training performance of Random Forest models on CPU architectures.
method Investigated and improved tree building algorithms (BFS, DFS, hybrid BFS-DFS) and proposed optimizations.
result The hybrid BFS-DFS algorithm outperforms both BFS and DFS, and is more robust.
Ensemble method for fast portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management from cash flow data.
method Regression trees for dynamic value process learning.
result Fast and accurate estimator with closed-form solution.
In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of …
Paper unifies subspace identification and DMD for dynamical systems.
problem Estimating dynamical models from data.
method Unified optimization and regression problems for SID and DMD.
result Proves equivalence of SID and DMD for optimal model construction.
Generative models for complex stochastic dynamics using adversarial learning.
problem Data-driven modeling of multistep stochastic dynamics.
method Adversarial learning with GANs and MMD for stable model classes.
result Stable generative models for long-time prediction and stochastic systems.
Method upgrades limit theorems to mixing limit theorems for dynamical systems.
problem Improving limit theorems for dynamical systems.
method General method for upgrading limit theorems to mixing limit theorems.
result Mixing limit theorems for specific subbundles of the Kontsevich-Zorich cocycle.
Enhances neural network dynamics to boost computational capacity.
problem Improving computational capacity of neural networks.
method Introducing Phase Transition Adaptation to drive system dynamics towards edge of stability.
result Consistently achieves enhancement in computational capacity over multiple datasets.
Machine learning with kernels for portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management in finance.
method Machine learning with kernels to learn the dynamic value process of a portfolio from cumulative cash flow data.
result Asymptotic consistency and finite sample error bounds demonstrated for finance applications.
Several new mutation-periodic quivers of period higher than 1 are introduced as well as the associated discrete dynamical systems. The reduction of these systems is developed using either a presymplectic or a Poisson approach. The presymplectic approach leads to a reduced system whose iteration map is symplectic with r…
New model predicts energy prices volatility by smoothing time variation and persistence.
problem Separate study of volatility's time variation and persistence.
method Dynamic persistence model that allows shocks with heterogeneous persistence to vary smoothly over time.
result Significantly improves volatility forecasts over state-of-the-art models.
This work addresses decentralized online optimization in non-stationary environments. A network of agents aim to track the minimizer of a global time-varying convex function. The minimizer evolves according to a known dynamics corrupted by an unknown, unstructured noise. At each time, the global function can be cast as…
Deep learning predicts contagion dynamics on complex networks.
problem Forecasting contagion dynamics on complex networks is challenging.
method Graph neural network learns local mechanisms from time series data.
result Deep learning offers new and accurate models of contagion dynamics.
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the v…