A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Shared workspace improves neural module coordination in deep learning.
We propose a novel neural network architecture, named the Global Workspace Network (GWN), which addresses the challenge of dynamic and unspecified uncertainties in multimodal data fusion. Our GWN is a model of attention across modalities and evolving through time, and is inspired by the well-established Global Workspac…
Spacematch matches office workers with suitable workspaces based on their preferences.
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology. In this paper we present a model-free RL algorithm to synthesize control policies that maximize the probability of satisfying h…
NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN,…
Hydra boosts efficiency for long-context reasoning in resource-constrained settings.
Tripod spiders' energy control analyzed for Hooke and Coulomb potentials.
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian clas…
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself a…
We analyse perception and memory, using mathematical models for knowledge graphs and tensors, to gain insights into the corresponding functionalities of the human mind. Our discussion is based on the concept of propositional sentences consisting of \textit{subject-predicate-object} (SPO) triples for expressing elementa…
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue…
Proposes Learnergy, a Python framework for energy-based machine learning.
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace. For non-trivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed thi…
New framework models epistemic uncertainty in GNNs using random sets.
JuliaConnectoR integrates Julia functions into R for deep learning.
TASO optimizes CNN models for memory-constrained devices.
TUV Austria proposes certification for ML applications to ensure reliability.
The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus is usually very narrow and limited to (i) inference -- i.e. how to efficiently execute already trained models and (ii) image classification networks…
This thesis explores GNNs, categorizing them into local and global approaches.
Generalizes global hyperbolicity to higher signatures and proves compactness.
Global methods outperform local in forecasting groups of time series, even in heterogeneous datasets.
In this note a proof is given for global existence and uniqueness of minimal surfaces of Lorentzian type from a cylinder into globally hyperbolic Lorentzian manifolds for given initial values up to the first derivatives.
New spacetimes found that are refocusing but not strongly refocusing.
Global and local blowups of manifolds are proven equivalent.
Global calculus for manifolds with boundary, solving evolution problems.
Study calculates global sections on complex curves.
Smooth convergence shown for curve diffusion flows.
We prove the global existence of Dirac-wave maps with curvature term with small initial data on globally hyperbolic manifolds of arbitrary dimension which satisfy a suitable growth condition. In addition, we also prove a global existence result for wave maps under similar assumptions.
Global optimization in Bayesian inference yields little additional benefit.
Combines global and local search for efficient global optimization with Gaussian processes.
Global implicit function theorem for Fréchet spaces, solving derivative loss problems.
New global section found for geodesic flows on convex hypersurfaces.
Improves global counterfactual explanations for model recourse.
We show global existence and convergence results for the pluriclosed flow on manifolds for which certain naturally associated tensor bundles are globally generated.
Graph products inherit Morse local-to-global property from their components.
Develops methods to calculate global index of real polynomials.
Study curve flows with global forcing terms using a distance comparison principle.
A new deep generative model captures global dependencies without supervision.
We show that the definition of global hyperbolicity in terms of the compactness of the causal diamonds and non-total imprisonment can be extended to spacetimes with continuous metrics, while retaining all of the equivalences to other notions of global hyperbolicity. In fact, global hyperbolicity is equivalent to the co…
We study global aspects of complete, non-singular asymptotically locally AdS spacetimes solving the vacuum Einstein equations whose conformal infinity is an arbitrary globally stationary spacetime. It is proved that any such solution which is asymptotically stationary to the past and future is itself globally stationar…
Global invariant for path structures and differential equations defined on torus.
Study describes global sections of chiral de Rham complex on compact Ricci-flat Kähler manifolds.
Study global geometry of toric nearly Kähler manifolds using multi-moment maps.
Smooth distributions on subcartesian spaces can be globally finitely generated.
Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce…
It has been shown that deep neural networks (DNNs) may be vulnerable to adversarial attacks, raising the concern on their robustness particularly for safety-critical applications. Recognizing the local nature and limitations of existing adversarial attacks, we present a new type of global adversarial attacks for assess…
Throughout economic history, the global economy has experienced recurring crises. The persistent recurrence of such economic crises calls for an understanding of their generic features rather than treating them as singular events. The global economic system is a highly complex system and can best be viewed in terms of …
Global models outperform local models in forecasting intermittent time series.