Discusses geometry problems for fun and learning.
problem Geometry problems and their solutions.
method Not original, but related to differential geometry course.
result Relation to differential geometry course.
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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.
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8 results for “treasurers”
The paper introduces deep learning for ALM, enhancing asset and liability management.
problem Optimizing asset and liability management for treasurers and other applications.
method Deep learning applied to ALM for optimal decision making.
result Enhanced ALM approach for better asset and liability management.
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental …
Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have a…
One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examplescs.LG
Transform images with adversarial examples for robust classification.
problem Adversarial examples mislead deep neural networks in image classification.
method Transform input images with adversarial examples from a pre-trained model.
result Significantly more robust against a wide range of attacks compared to state-of-the-art methods.
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuningcs.CV
Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insuf…
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
Expanding models in neural fictitious play improves reinforcement learning efficiency and robustness.
problem Forgetting old opponents after training new ones in reinforcement learning.
method Train a single model with sub-models and a selector, expanding the model with new sub-models and updating the selector to maintain a behavior strategy.
result Improves learning efficiency and robustness of neural fictitious play.