IPAB Workshop-20/05/2021 Title: Taming Timely Inverses Abstract: Last year, at the IPAB seminar, I spoke about the importance of timely (rapid) approximations of physical simulations. Currently, I am excited about developing rapid approximations for inverse problems involving physical simulation. Despite troves of methods for solving inverse problems, sparingly few consider timeliness as a measure of error. In many real-time applications, obtaining an accurate or precise answer is only useful if it can be estimated within a narrow temporal window. Conversely, rapid estimates are indeed less useful if they are not accompanied by analyses of popular error criteria such as accuracy and precision. In my talk this week, I will present a couple of recent works [1,2] that are early steps in this direction. In addition, I will also briefly discuss other recent work [3,4,5]. [1] Q-NET: A Network for Low-Dimensional Integrals of Neural Proxies Kartic Subr https://arxiv.org/pdf/2006.14396 [2] IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates Tatiana Lopez-Guevara, Michael Burke, Nicholas K. Taylor, Kartic Subr https://arxiv.org/pdf/2012.01925 [3] Action sequencing using visual permutations Michael Burke, Kartic Subr, Subramanian Ramamoorthy https://arxiv.org/pdf/2008.01156 [4] Jittering Samples using a kd-Tree Stratification Alexandros D. Keros, Divakaran Divakaran, Kartic Subr https://arxiv.org/pdf/2002.07002 [5] On Improved Training of CNNs for Acoustic Localisation Elizabeth Vargas, James Hopgood, Keith Brown, Kartic Subr https://homepages.inf.ed.ac.uk/ksubr/Files/Papers/TASLP21.pdf May 20 2021 13.00 - 14.00 IPAB Workshop-20/05/2021 Kartic Subr Blackboard Collaborate
IPAB Workshop-20/05/2021 Title: Taming Timely Inverses Abstract: Last year, at the IPAB seminar, I spoke about the importance of timely (rapid) approximations of physical simulations. Currently, I am excited about developing rapid approximations for inverse problems involving physical simulation. Despite troves of methods for solving inverse problems, sparingly few consider timeliness as a measure of error. In many real-time applications, obtaining an accurate or precise answer is only useful if it can be estimated within a narrow temporal window. Conversely, rapid estimates are indeed less useful if they are not accompanied by analyses of popular error criteria such as accuracy and precision. In my talk this week, I will present a couple of recent works [1,2] that are early steps in this direction. In addition, I will also briefly discuss other recent work [3,4,5]. [1] Q-NET: A Network for Low-Dimensional Integrals of Neural Proxies Kartic Subr https://arxiv.org/pdf/2006.14396 [2] IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates Tatiana Lopez-Guevara, Michael Burke, Nicholas K. Taylor, Kartic Subr https://arxiv.org/pdf/2012.01925 [3] Action sequencing using visual permutations Michael Burke, Kartic Subr, Subramanian Ramamoorthy https://arxiv.org/pdf/2008.01156 [4] Jittering Samples using a kd-Tree Stratification Alexandros D. Keros, Divakaran Divakaran, Kartic Subr https://arxiv.org/pdf/2002.07002 [5] On Improved Training of CNNs for Acoustic Localisation Elizabeth Vargas, James Hopgood, Keith Brown, Kartic Subr https://homepages.inf.ed.ac.uk/ksubr/Files/Papers/TASLP21.pdf May 20 2021 13.00 - 14.00 IPAB Workshop-20/05/2021 Kartic Subr Blackboard Collaborate
