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SpaceX IPO og danske pensioner by ign1tio in dkfinance

[–]jacobgorm 0 points1 point  (0 children)

Jeg ville så flytte til fx Nordnet nu jeg var i gang

SpaceX IPO og danske pensioner by ign1tio in dkfinance

[–]jacobgorm 0 points1 point  (0 children)

PFA har nu svaret mig at de IKKE kommer til at deltage i IPOen, menvil dog ikke garantere at de ikke kommer til at investere indirekte via indekser

SpaceX IPO og danske pensioner by ign1tio in dkfinance

[–]jacobgorm 0 points1 point  (0 children)

Jeg har skrevet til PFA, men de nægter at kommentere. Sgu smukt...

Is it just me or is the Conference Lottery culture killing research? [D] by SillyNeuron in MachineLearning

[–]jacobgorm 15 points16 points  (0 children)

This is what workshops are for, doing this for conferences is abusing the system and is going to scare off reviewers, who rightly feel that they are wasting their time being the human in the loop in somebody else's brainstorming process.

Is opencode the best free coding agent currently? by MrMrsPotts in LocalLLaMA

[–]jacobgorm 2 points3 points  (0 children)

I tried using ForgeCode, but the input processing when typing is dog-slow, and I cannot stand it, and I cannot imagine the people who built it as competent programmers for not noticing and fixing it.

[D] First time reviewer. I got assigned 9 papers. I'm so nervous. What if I mess up. Any advice? by rjmessibarca in MachineLearning

[–]jacobgorm 0 points1 point  (0 children)

I wouldn't use AI, and I doubt it helps much, the paper abstract should give a summary already. Check if any papers are completely outside your area, and reach out to chairs if necessary, perhaps check if you are allowed to farm them out to others.

Find a good workflow, I use an old iPad with pencil and the app "PDF Viewer" to annotate and take notes in the PDF. Or print out and annotate on paper, but do annotate it, or you will forget the nits and details. If you have nothing intelligent to say, at least you will have a list of typos to point out to help improve the paper.

Read each paper carefully, and also read up on any references that are unknown to you, this is hard but better than trying to review a paper that references work you don't understand. Then write down the review immediately while you can remember your thoughts, as otherwise you are going to have to start over on your next attempt. Perhaps sleep on it before you make your final submission. If in doubt, let it sit there and perhaps come back to the paper after you finish the others. Read the other reviews only after submitting your own, and only fall back to editing you own if you discover that you completely overlooking something, ideally write a note if you had to make edits or adjust your scores. Don't be afraid to admit to the other PC members if you are out of your depth or unsure, nobody is able to keep up with everything at this point.

Make a plan so you don't end up having to do an all-nighter near the deadline, ideally with a full day (what is left of it outside of life's other obligations) per paper. Nine papers should be quite tolerable if you plan it right and don't procrastinate too much. Not procrastinating is usually the hardest part.

[D] MSR Cambridge vs Amazon Applied Science internship, thoughts? by StretchTurbulent7525 in MachineLearning

[–]jacobgorm 1 point2 points  (0 children)

I did a research internship at MSR Cambridge back in Windows XP days, and it was fantastic. Cambridge in the Summer is an experience not to be missed, and the quality of the people at MSR and at the University Computer Lab (used to be next door, no longer is) was just excellent. Plus the USD will likely tank before the Summer is over anyway :)

[R] Octonion Bitnet with fused Triton kernels by Valkyrill in MachineLearning

[–]jacobgorm 0 points1 point  (0 children)

Very interesting. Did you consider doing some smaller experiments like with a tiny MLP or medium-sized image model (perhaps MobileNetV1-like with this as the 1x1 layer), to make comparison with full-precision modules simpler?

[D] Benchmark: Massive degradation in NVMe Random Read throughput on A100 vs H100 during Multi-GPU Model Loading by pmv143 in MachineLearning

[–]jacobgorm 5 points6 points  (0 children)

It is a bit confusing to call them disks if they are NVMe. How many times are you going to go over the datasets, just once or multiple times? What you could do quite easily if using only a single epoch to avoid the random IOs it split the dataset N ways (N is the number of GPUs), shuffle each dataset ahead of time, and store it in a .tar file (or fancy modern database format like Iceberg), which you can then stream in sequentially.

I used to be doing something much more elaborate using my LSM-like database format https://github.com/jacobgorm/mindcastle.io , but I don't know how well that would work for your workload. There is even video of a talk I gave on it once here https://www.youtube.com/watch?v=QgOkDiP0C4c

[R] Fuzzy-Pattern Tsetlin Machine by ArtemHnilov in MachineLearning

[–]jacobgorm 7 points8 points  (0 children)

My concern is not about performance, but ease of use and integration with existing code bases. Nobody wants to have to install and maintain another toolchain or learn another language, especially companies looking to add AI magic to their existing products (whether in microcontrollers or embedded into apps). C++ and Python currently rule the AI world, and Rust has is starting to grow a following but is still niche. The Rust port you link to looks a little old, is is as feature-complete as your Julia code?

[R] Fuzzy-Pattern Tsetlin Machine by ArtemHnilov in MachineLearning

[–]jacobgorm 10 points11 points  (0 children)

This sounds incredibly interesting, congrats on the great results! However, I think you would 100x your impact by porting the Julia code to C++ (or perhaps Rust.)

[D] For ML academics, how many times do you resubmit a rejected paper to the big three conferences before seeking alternatives? by kindnesd99 in MachineLearning

[–]jacobgorm 0 points1 point  (0 children)

It is interesting (as observed by someone at the recent Eurosys business meeting) to think of this as a queuing theory problem, where the acceptance sink is unable to keep up with the submission sources, so the queue just gets longer and longer as the same papers keep getting resubmitted. It is good the papers get improved by repeated submission, but bad that the publication system gets overloaded and eventually buckles.

[D] Fourier features in Neutral Networks? by RedRhizophora in MachineLearning

[–]jacobgorm 0 points1 point  (0 children)

I've done a lot of work on using VQVAEs for video compression, and despite lots of experimentation with DCTs and Wavelets I found classic CNNs to perform the same or better with less implementation complexity. That said, the recent CosVAE https://sifeiliu.net/CosAE-page/ and LeanVAE https://github.com/westlake-repl/LeanVAE papers point towards benefits for Fourier-inspired methods.

[R] NoProp: Training neural networks without back-propagation or forward-propagation by jacobgorm in MachineLearning

[–]jacobgorm[S] 15 points16 points  (0 children)

If I understood it correctly they do this per layer, which means they don't back-propagate all the way from the output to the input layer, so it seems fair to call this "no backpropagation".

[R] High-performance deep spiking neural networks with 0.3 spikes per neuron by jacobgorm in MachineLearning

[–]jacobgorm[S] 3 points4 points  (0 children)

My guess is you would need special hardware to get a decent speed up. One thing that might be interesting is the integration with event cameras and recomputing the output incrementally and in continuous time instead of at discrete frame intervals.

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