no code implementations • 7 Jan 2023 • Zongmin Liu, Jirui Wang, Jie Li, Pengda Liu, Kai Ren
However, indoor scenes are usually complex and there are many types of interference factors, leading to great challenges in the multiple targets detection.
no code implementations • 5 Jul 2021 • Sumegha Garg, Pravesh K. Kothari, Pengda Liu, Ran Raz
We show that any learning algorithm for the learning problem corresponding to $M$, with error, requires either a memory of size at least $\Omega\left(\frac{k \cdot \ell}{\varepsilon} \right)$, or at least $2^{\Omega(r)}$ samples.
