arXiv subjects

Orathai Sangpetch

Publications and source records attributed to Orathai Sangpetch.

3 recordsLinked to original sources

arXiv2025

Solving Functional Optimization with Deep Networks and Variational Principles

Can neural networks solve math problems using first a principle alone? This paper shows how to leverage the fundamental theorem of the calculus of variations to design deep neural networks to solve functional optimization without requiring training data (e.g., ground-truth optimal solutions). Our approach is particularly crucial when the solution is a function defined over an unknown interval or support\textemdash such as in minimum-time control problems. By incorporating the necessary conditions satisfied by the optimal function solution, as derived from the calculus of variation, in the design of the deep architecture, CalVNet leverages overparameterized neural networks to learn these optimal functions directly. We validate CalVNet by showing that, without relying on ground-truth data and simply incorporating first principles, it successfully derives the Kalman filter for linear filtering, the bang-bang optimal control for minimum-time problems, and finds geodesics on manifolds. Our results demonstrate that CalVNet can be trained in an unsupervised manner, without relying on ground-truth data, establishing a promising framework for addressing general, potentially unsolved functional optimization problems that still lack analytical solutions.

cs.LG↗

arXiv2023

PhyOT: Physics-informed object tracking in surveillance cameras

While deep learning has been very successful in computer vision, real world operating conditions such as lighting variation, background clutter, or occlusion hinder its accuracy across several tasks. Prior work has shown that hybrid models -- combining neural networks and heuristics/algorithms -- can outperform vanilla deep learning for several computer vision tasks, such as classification or tracking. We consider the case of object tracking, and evaluate a hybrid model (PhyOT) that conceptualizes deep neural networks as ``sensors'' in a Kalman filter setup, where prior knowledge, in the form of Newtonian laws of motion, is used to fuse sensor observations and to perform improved estimations. Our experiments combine three neural networks, performing position, indirect velocity and acceleration estimation, respectively, and evaluate such a formulation on two benchmark datasets: a warehouse security camera dataset that we collected and annotated and a traffic camera open dataset. Results suggest that our PhyOT can track objects in extreme conditions that the state-of-the-art deep neural networks fail while its performance in general cases does not degrade significantly from that of existing deep learning approaches. Results also suggest that our PhyOT components are generalizable and transferable.

cs.CV↗

arXiv2023

AI For Fraud Awareness

In today's world, with the rise of numerous social platforms, it has become relatively easy for anyone to spread false information and lure people into traps. Fraudulent schemes and traps are growing rapidly in the investment world. Due to this, countries and individuals face huge financial risks. We present an awareness system with the use of machine learning and gamification techniques to educate the people about investment scams and traps. Our system applies machine learning techniques to provide a personalized learning experience to the user. The system chooses distinct game-design elements and scams from the knowledge pool crafted by domain experts for each individual. The objective of the research project is to reduce inequalities in all countries by educating investors via Active Learning. Our goal is to assist the regulators in assuring a conducive environment for a fair, efficient, and inclusive capital market. In the paper, we discuss the impact of the problem, provide implementation details, and showcase the potentiality of the system through preliminary experiments and results.

cs.AI↗