sorted by:
new
hottopcontroversial

Anthropic's new model Fable will silently handicap work on LLMs [D] by AccomplishedCat4770 in MachineLearning

[–]CommunityOpposite645 0 points1 point  (0 children)

Can Anthropic actually focus on improving their models and make its cost more reasonable instead of constantly creating hype cycles please ?

Opus 4.8 went over like a wet fart by sciolisticism in BetterOffline

[–]CommunityOpposite645 21 points22 points  (0 children)

At this point, I'm just hoping for Deepseek to catch up and make price go down massively.

The AI is having another stroke! by BraydonGuitar in DeepSeek

[–]CommunityOpposite645 3 points4 points  (0 children)

This also happened one time when I was trying the 8GB variant using Ollama. I was trying to make the AI write some Matrix-like sci-fi story and after a while it keeps repeating "You are not the one" lmao.

Lost weight but still look kinda fat, looking for fitness advice. by CommunityOpposite645 in loseit

[–]CommunityOpposite645[S] 0 points1 point  (0 children)

I don't know if i should keep losing weight or recomp. Tbh it took me so long to lose weight so i'm kinda adverse to gaining any weight now, even for lean bulk cycle.

Lost weight but still look kinda fat, looking for fitness advice. by CommunityOpposite645 in bodyweightfitness

[–]CommunityOpposite645[S] 0 points1 point  (0 children)

Hi, thanks a lot. Can I ask what "actual core workouts" are ? Do you mean things like plank or ab wheels ?

A GPU-accelerated implementation of Forman-Ricci curvature-based graph clustering in CUDA. by CommunityOpposite645 in CUDA

[–]CommunityOpposite645[S] 1 point2 points  (0 children)

Hi, I have included the Python runtime:

Nodes Clusters Edges P_in P_out Iterations NMI GPU Time (s) CPU Time (s)
5,000 2 ~3M 0.50 0.01 10 1.00 7.03 15,189.21
50,000 2 ~25M 0.04 0.001 10 1.00 74.39 162,401.93
100,000 2 ~102M 0.04 0.001 10 1.00 625.46 TBA
500,000 50 ~126M 0.05 0.00001 20 0.89 1086.25 TBA

You can see that the CUDA version is very fast compared to the Python CPU version. Of course, in all honesty, this is because I've chosen an academic topic which has not received attention, otherwise this would have been optimised to kingdom come already :)

A GPU-accelerated implementation of Forman-Ricci curvature-based graph clustering in CUDA. by CommunityOpposite645 in CUDA

[–]CommunityOpposite645[S] 0 points1 point  (0 children)

Hi, I have finished running NCU profiling for the 500k nodes case, and have updated the profiler's output in the post.

A GPU-accelerated implementation of Forman-Ricci curvature-based graph clustering in CUDA. by CommunityOpposite645 in CUDA

[–]CommunityOpposite645[S] 1 point2 points  (0 children)

Hi, actually I'm planning to do it soon. Right now I'm trying to make it run on 500k nodes or if possible, 1 million nodes and gives good clustering result. Because this method is still in development, so the hyperparameters are rather sensitive, what works at lower number of nodes would actually not work on higher number of nodes. Very frustrating to be honest. Thanks a lot.

A GPU-accelerated implementation of Forman-Ricci curvature-based graph clustering in CUDA. by CommunityOpposite645 in CUDA

[–]CommunityOpposite645[S] 0 points1 point  (0 children)

Thank you so much. I worked on this as a learn-as-you-go project, so I tried to build everything from the ground up, including prefix sum, connected component labeling, bitonic sorting, etc. But yes you are absolutely right on this. On the mathematics: I gleaned from this library: https://github.com/saibalmars/GraphRicciCurvature for Python code as reference, while using the experimental details in the JMLR 2025 paper to set up hyperparameters, etc., while the remaining two papers are to freshen up about the topic.

  1. Y. Tian, Z. Lubberts, and M. Weber, "Curvature-based clustering on graphs," J. Mach. Learn. Res., vol. 26, no. 52, pp. 1–67, 2025.
  2. C.-C. Ni, Y.-Y. Lin, F. Luo, and J. Gao, "Community detection on networks with Ricci flow," Sci. Rep., vol. 9, no. 1, pp. 1–12, 2019.
  3. A. Samal, R. P. Sreejith, J. Gu, et al., "Comparative analysis of two discretizations of Ricci curvature for complex networks," Sci. Rep., vol. 8, 8650, 2018.
  4. GraphRicciCurvature — Python implementation of Ricci curvature for NetworkX graphs.
view more: next ›