I would posit that these models should be able to guess the real-life user from a set of their forum posts then. Instead of geoguessing, it's op-guessing.
Something I don't see mentioned here but is implicitly assumed is that the candidate wants the job. Given the lottery of passing leet coding interviews, interviews are a place to practice interviewing. Some candidates may not want the job but simply want to try different things during the interview and see what happens with the goal of practice for an interview for a role they really care about.
I know very little of css and to me it seems like a configuration file for rendering text, similar to changing default fonts ornsizes for matplotlib plots using plt.rcParams. How does this do inage blurring then?
If you read the article, it explains exactly how the technique works.
One of the many ways CSS allows you to customize formatting is to change the background style of elements. In addition to just using a solid color or image, you can specify a procedural gradient. And by superimposing several such gradients, you can make a very blurry approximation of an image.
CSS also includes a basic expression language which allows evaluating simple arithmetic expressions. So you can encode all the blurred image's parameters as a packed integer in a single compact CSS property per image, and use rules to define the gradients in terms of that integer.
Note that CSS is not used to compute the blurred image representation itself -- you have to do that separately. (Even if you could do it in pure CSS, the whole point is to show a blurred preview image before the image itself is downloaded to the browser, so doing it in CSS would defeat the purpose.)
> [It] seems like a configuration file for rendering text...
A more accurate mental model might be, "a declarative language for styling HTML elements," where "styling" is very broad. You can make user interfaces that show and hide elements, have animations, etc. triggered by clicking buttons without a single line of JavaScript. It's a lot more powerful than the configuration parameters to plotting functions, in my book it's a programming language rather than a configuration language.
The computer vision community needs an dataset like this for evaluation... train in one domain and test on another. The best we have now are thr imagenet r and c datasets. Humans have no issues with domain adaptation with vision, but comouter vision models struggle in many ways sti including out of domain images.
>The Fourier transform of a Gaussian is a Gaussian, which is very helpful when you need to estimate the frequency of a harmonic signal (like speech) with a wavelength shorter than half your window, but just barely.
I get the gaussian link. But, can you explain your point with more detail?
The log of a Gaussian is a parabola, which makes finding where exactly a peak in the spectrum lies a question of solving a quadratic equation.
My plan was to detect the frequency of the speaker by counting (with weights) which distance between peaks is the most common. I wanted to avoid calculating the power cepstrum as I felt that I was running out of computing power on the devices already[0] - a mistaken belief in the long run, but I was too proud of my little algorithm and how stable it was to let go.
[0] Sending raw sample data to a more powerful machine was out of the question as I wanted to remain within the bandwidth offered by Bluetooth at the time due to power consumption considerations.
Why would any startup enter this space? Honest question. There are so many business in this space coupled with cyclic funding cycles. Seems like a bad market to invest in.
Feel free to reply and we can chat more as I've thought deeply about this topic. Here are some cliff notes though if you want a quick answer (say for calibration).
1. Dod labs and defense contractors are great places to dive into topics pending you are inexpensive. You will likely be paired with other people who can sit and thinking deeply with you. As you progress though, you will be pushed into management, forced to being in money, and pulled many ways preventing your deep thinking. This is when most good scientists leave.
2. Be confident in your job search about what you are looking for. Don't be afraid to say you want 85% deep technical work and 15% reporting to stakeholders. This will be polarizing to a lot of employers but those who want you will quickly scoop you up when u get connected to them.
3. The market is terrible for r and d right now across almost all sectors. Most r and d labs are looking 3-6 months out which means they want people qoth a specific background to build prototypes amd fail quickly. This will be existing scientists in the company but rarely any new hires. i interviewed at Amazon and found this to be the case as an example (I turned down the role).
4. Wait until the market improves but keep looking while doing item 2.
Overall, i like this guys papers but they strike me as someone who is very smart but hasnt looked through the literature carefully. Many of the techniques he is proposing were already done about 5-6 years ago. However, I imagine that because the field is flooded with new humans, they are not aware of this research or think it will lead to a fruitful end (which many other researchers have already thought of this and it didn't lead anywhere hence why it was abandoned). Overall, it seems we are starting to recycle ideas because there isnt enough lit review and or mentoring from senior deep learning / ML folks who can quickly look at a paper and tell the author where the work has been already investigated.
Reviving old ideas and comparing them to SOTA is not necessarily bad especially if they provide benefits over the SOTA model. It brings the old ideas into the community idea cache if you will. It’s somewhat annoying if the authors do it thinking it’s a novel idea when it fact it’s a 20-30 year old one.
This reminds me of some HN comments about rocketry ideas and in the thread one of the comments was “Everything in rocket science has been theorized/tried by some Russian scientist 40-50 years ago” and it still gives me a chuckle.
> Overall, it seems we are starting to recycle ideas because there isnt enough lit review and or mentoring from senior deep learning / ML folks who can quickly look at a paper and tell the author where the work has been already investigated.
Arguably, the literature synthesis and knowledge discovery problem has been overwhelming in many fields for a long time; but I wonder if, in ML lately, an accelerated (if not frantic) level of competition may be working against the collegial spirit.
I think it's been accelerated by the review community being overwelmed and the lack of experienced researchers with the combination of classic ML, deep learning, transformers, and DSP backgrounds -- a rare breed but sorely needed.
I am with you! Been programming since I was 10 and have 20YoE. Many of my prototypes have grown into full fledged products, I have 40+ published papers, and I am regularly sought out for advice and help by those who know me. Everyone i have been, I am always told I am a good catch.
However, I won't do leet coding. I want to hear about why I should come work for u. What about my works makes u think I could help ubm with your problem. Then let's have a talk about your problems and where I can create value for you.
My experience in hiring is that leet coders are good one trick ponies. But long term don't become technical peers.
Part of the problem is there just aren't a lot of people out there who can correctly judge that level of experience, and looking up the spectrum tends to simply look weird.
I agree. Do you have any thoughts on how to mitigate this? After all, its in your best advantage and the companies to hire someone with talents because of the value they can bring.
Unfortunately (at least in my opinion) the way to mitigate it is make contacts, friends, colleagues etc. Meet people, stay in touch with them. In my experience relationships are by far the most important aspect of a career. I'm not naturally someone who forms these relationships easily, but it's by far the most valuable and important thing I've done in terms of the jobs I've had.
It's VERY rare for me to work with people capable of even seeing the level I'm working at, sad but true. I suspect my solutions simply look weird to them, maybe with a touch of NIH-syndrome; even if they eventually grow to appreciate the integrity of the code. And it's difficult to see code that's never written, which is feel is the biggest win I bring to the table.
This means I'm mostly judged by my ability to perform mindless drone work.
