Gradient-based trajectory planning

this semester has been completely crazy for me, and i anticipate that this madness will only worsen over the next couple of months. of course, because of this crazy schedule, my brain started to revolt by growing a doubt inside me on how much i trust gradient descent. crazy, right? yes. i then succumbed to this temptation and looked for some simple example to test my trust in gradient descent. yes, i know that i should never doubt our lord Gradient Descent, but my belief is simply too weak. so, i decided to use gradient descent for simple trajectory planning

A short thought on watermarking

so, it looks like watermarking is a thing that is coming back to its (controversial) life. the idea of watermarking is to enable content producers to mark their own contents so as to track where those contents are being consumed without introducing too much of disruption. one of the simplest watermarking techniques i run into quite often is on a plan with their entertainment system; when you watch a movie on an airplane, you often notice the airline code (e.g. “DL” in the case of Delta) embroiled on the screen once a while. i presume the heightened interest in watermarking

Follow-up donation after 2 years

after receiving the Samsung Ho-Am Prize 2.5 years ago (early 2021), i made a few small donations here and there; i donated approximately \$85,000 to KAIST to establish a small scholarship for female students in computer science in honour of my mom, \$85,000 to Soongsil University for my dad who since then has retired from Soongsil University after more than 30 years there as a professor of korean literature and language, €30,000 EUR to Aalto University’s computer science for establishing a small scholarship to support non-EU students, \$30,000 CAD to Mila, and \$50,000 USD to CIFAR for supporting female researchers

Expectile regression

i often find myself extremely embarrassed by myself, because i learn of concepts in machine learning that i should’ve known as a professor in machine learning but had never even heard of before. one latest example was expectile regression; i ran into this concept while studying Kostrikov et al. (2021) on implicit Q learning for offline reinforcement learning together with Daekyu who is visiting me from Samsung. in their paper, Kostrikov et al. present the following loss function to estimate the $\tau$-th expectile of a random variable $X$: $$\arg\min_{m_{\tau}} \mathbb{E}_{x \sim X}\left[ L_2^\tau (x – m_{\tau}) \right],$$ where $L_2^\tau(u) =

Defining emergence

so, apparently, emergence has become a hot topic on twitter while i was away in Kigali attending ICLR, moto-taxing in Kigali, injuring myself and breaking my phone running and tracking, seeing a majestic group of gorillas and being back at AIMS Rwanda after 4 years. the mountain gorillas were majestic. i do not want to discuss any particular paper/tweet/blog, because this topic seems to attract a weird set of people arguing for weird things, when in fact there are just a couple of different views into a single phenomenon, which is only natural in science and engineering. that said, if