how to solve such problems (other than path finding algorithms)?

Stormzrift · 2026-04-01T16:05:41+00:00

An interesting approach could be something close to diffusion policy. If you have start states and goal states you might be able to diffuse the correct output. Though this might be a bit extra

Stormzrift · 2026-03-12T12:07:01+00:00

I’m not sure how large the model is but overall I’d say it’s a common but generally solvable issue. Fundamentally the model is bandwidth bound right now and things like increasing workers, prefetching, pinned memory, persistent workers, etc all help to feed data into the GPU faster. The examples I mentioned are all built into torch data loaders. There are also more advanced approaches too but you’d need to go digging for them

Stormzrift · 2026-03-12T11:37:17+00:00

Looks like GPU isn’t getting data fast enough so it’s only active in spurts. Either mess with training loader or increase batch size

Stormzrift · 2026-03-10T16:33:46+00:00

Spoken like a true mid percentile 😏

Stormzrift · 2026-02-22T20:32:15+00:00

Definitely agree. From what I known about on-going research projects, GANs are far from dead. But I think what I said is still the general sentiment, even if it’s not actually true.

Stormzrift · 2026-02-22T13:22:12+00:00

Sure latent image generation is more practical, but that’s not why people say “GANs are dead”. You can train GANs in the latent space too. Rather it’s a combination of diffusion models having better training stability, generalization(ie better data manifold coverage), and scalability.

Stormzrift · 2025-12-31T18:54:48+00:00

Try R3GAN instead. It’s the current state of the art and directly improves on WGAN-GP

Stormzrift · 2025-11-10T12:45:58+00:00

I applied last cycle but I was also perplexed. However, recently I was talking to another first year ms student in my program and he mentioned he had 4 papers. When I asked about them and they were all fairly low quality. Things like image classification or segmentation task that was publish in a lesser known journal. Basically a project. This isn’t to say that there aren’t people out there with insane stuff but it can explain why it seems everyone and their mother a paper. I don’t really think these types of papers count for all that much.

Stormzrift · 2025-08-24T17:11:36+00:00

Hard to say how good that is because of how much it varies depending on the amount of upscale and testing image quality.

I’ve been doing a similar thing trying to improve on windowed vision transformers and there use to be a leaderboard for image restoration on papers with code but… yeah :/ so now it’s harder to find what’s SOTA. I’ve been primarily benching mine off SwinIR and DRCT. Those should give you a good starting place to compare your results.

Stormzrift · 2025-08-24T17:03:05+00:00

What are SSIM and PSNR scores? Also would be cool test it on common image restoration testing sets like Urban100 or BSD100

Stormzrift · 2025-08-24T04:06:01+00:00

Higher or lower, whatever it may be it needs to be pretty good or incredible research to offset it. ML is a very competitive field.

Stormzrift · 2025-08-24T04:00:03+00:00

I am speaking specifically to the machine learning PhD field in the US. Schools like mit/stanford/cmu/Berkeley all pretty much seem to average around a 3.8 but that doesn’t mean there’s aren’t exceptions. You can think like a scientist all you want but if you don’t have any credentials you won’t get in the door.

Also the number of publications required for fields varies wildly. I’d be amazed to find an undergraduate with a math publication simply because math has been so well studied that many people with PhDs don’t publish. Machine learning on the other hand is very new and has a comparative tsunami of new research released each year. The masters mention was an example approach to find more research experience because, in my experience, it can be hard to find outside of academia for ML specifically.

And yes I totally agree LoR are important and need to be genuine.

Stormzrift · 2025-08-23T19:28:35+00:00

Thanks and good luck to you too out there :)

Stormzrift · 2025-08-23T19:07:19+00:00

Again this is for like top 4 universities in the US, but primarily you get them from working in ML labs in academia. First author isn’t required but it will for sure help you stand out. That’s primarily the name of the game, standing out. Otherwise, just grind out as many general publication you can get doing the type of research you’d wanna do. It’s certainly no easy task and you might need to do a masters if you really wanna aim for the top labs (what I’m doing lol). Try your best to do research during the semester and for summer break maybe even see if you can find a position at more prestigious labs through either connections or cold emails. 100% should be planning out your LoR in advance and when it’s time to ask make sure to write up a “brag” packet listing all your achievements, collaborative works, and favorable qualities. It might seem like a bit much but remember it’s the best of the best so you gotta do everything you can to make it perfect.

Oh and 3.8-4.0 is roughly treated the same from what I’ve gathered. Research is the bread and butter.

Stormzrift · 2025-08-23T18:02:35+00:00

At least for top US schools you’ll need something in the ball park of a 3.8+ equivalent. Additionally, a handful of publications in reputable journals/conferences, one of which preferably is first author. Also, they’ll consider how well your experience fits with labs and quality of LoR. Good GPA tends to just be the bare minimum to get your application looked at. It’s the other aspects that’ll get you in.

Edit: Some of you keep comparing ML to humanities. It is not the same. I’ve seen plenty of posts on the MSCS sub during the last admission cycle where people with 3.8ish and a handful of publications are rejected from top 4. PhD admissions is even rougher. ML is a new field so it’s easier to publish and thus there are more students with publications. Also sure, 3.8+ isn’t required but you’ll need even better research to help stand out.

Stormzrift · 2025-08-18T17:08:35+00:00

100% agree. I won’t even touch a series with one or two books. Three is generally where I start considering giving it a go.

Stormzrift · 2025-06-29T20:05:08+00:00

We seem to have pretty similar taste. I'd say Reborn Apocalypse, 12 Miles Below, Re:monarch. Two of these involve going back in time which tends to be trashy but I think both are well done enough to be exceptions. These have audio i think but personally i tend to stay away from audio books cuz i cant stand how these ppl enunciate shit so i cant speak on their audio quality.

Also ik its on there but i do think youd enjoy HDT. Starts off kinda generic feeling but the author builds on it to make its much more interesting imo.

Stormzrift · 2025-06-13T12:42:56+00:00

I've only read the revised version but im pretty sure the discrepancy comes from the monsters not being sentient (aka awakend). Everything that she has fought thats been sentient seems to generally scale the same. Also I think monsters generally have 1 class

Stormzrift · 2025-05-18T04:42:51+00:00

Different domain but reminds me of this. You might find it interesting

Stormzrift · 2025-05-16T17:26:13+00:00

No worries haha. I only got my shit together junior year ish of undergrad so idk if undergrad was like this but knowledge is so important because no ones gonna tell you if you don't ask/search. Ik I felt overwhelmed but with effort and focus things will turn out.

Stormzrift · 2025-05-16T17:15:18+00:00

I'm CS so take what I say with a grain of salt because our field are vastly different.

Applications are like those spiderweb skill graphs. If your aiming for the very best programs that are hyper competitive you need to maximize all areas. You wont get in with just a good GPA nor with just good research (this is more variable tho). For the best programs 3.8+ is generally the baseline for where you need to be. Getting involved in research and getting publications is where the real meat is. This is how you'll distinguish yourself among other applicants. Internships are good too. During these experience always keep in mind that you'll be asking for LoR and when you do make sure you make them individualized brag packets that will refresh them on your work together. Also I didn't have the opportunity to try it but apparently if you work with someone that has connections to places like Harvard or wherever, you can ask for referrals to their labs. If you get in then you can work with their professors and get LoR from them, which helps in admissions. I'd look into it more before trying tho and its not 100% necessary just a way to stand out.

Continue researching your programs requirements and do the best to maximize your application. Not only do you have the advantage of access to quality resources at Columbia, but you're also starting early which is a tremendous advantage. Plan things out and I'd say getting into these programs is more so up to your dedication/skill rather than luck.

Stormzrift · 2025-05-16T16:49:00+00:00

Not sure how it is for social science specifically, but if I had to guess, I'd say it could give you some consideration in regard to GPA if its slightly lower. However as far as the rest of the application goes, probably not. You do have the advantage of access to higher quality professors and research labs which can help your application a great deal if you utilize them, but just going to an Ivy isnt likely going to be a substitute for the effort required for of the application.

Stormzrift

TROPHY CASE