5 papers
One-Shot Localisation of the Global Minimum of a Noisy One-Dimensional Function: An Iterative Neural Minimizer Compared with Set Transformers and Classical Estimators
Qusay Muzaffar, David Levin, Michael Werman
We study a passive form of global optimisation: from twenty noisy samples of an unknown one-dimensional function, predict where its global minimum lies, with no further queries. We…
Exponential Convergence of Deep Composite Polynomial Approximation for Cusp-Type Functions
Kingsley Yeon, Steven B. Damelin, Michael Werman
We investigate deep composite polynomial approximations of continuous but non-differentiable functions with algebraic cusp singularities. The functions in focus consist of finitely…
CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis
Benjy Friedmann, Michael Werman
Point cloud processing poses two fundamental challenges: establishing consistent point ordering and effectively learning fine-grained geometric features. Current architectures rely…
The Fibonacci Network: A Simple Alternative for Positional Encoding
Yair Bleiberg, Michael Werman
Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional E…
Beyond the Benchmark: Detecting Diverse Anomalies in Videos
Yoav Arad, Michael Werman
Video Anomaly Detection (VAD) plays a crucial role in modern surveillance systems, aiming to identify various anomalies in real-world situations. However, current benchmark dataset…