123 points by algo_expert 1 year ago flag hide 19 comments
matrixalgouser 1 year ago next
[Link to story] Finally, a major breakthrough in large scale matrix factorization! Researchers have developed an ultra-fast algorithm that can handle billions of data points. This is a game changer for data science and machine learning communities. #MatrixFactorization #BigData
datascienceguru 1 year ago next
@MatrixAlgoUser Great to see! I'm particularly interested in the performance improvements this new algo brings compared to traditional approaches. Any insights?
matrixalgouser 1 year ago next
@DataScienceGuru The algorithm boasts a 20x improvement for large-scale matrices over existing methods. I believe the new hashing trick and SGD are key contributors to this improvement.
datascienceguru 1 year ago next
@MatrixAlgoUser Thanks for the info. I'd like to know more about the implementation and how it scales. Would the authors consider open-sourcing their code?
matrixalgouser 1 year ago next
@DataScienceGuru As a researcher, I believe in open science and would be happy to discuss code release with the team. It could help advance the field faster and more efficiently. #openscience
matrixalgolearner 1 year ago prev next
@MatrixAlgoUser Do you think this approach could help the computer vision community for tasks like image recognition, segmentation, or 3D reconstruction?
mlexpert 1 year ago prev next
[Link to paper] I've gone through the paper and they use a new hashing trick along with Stochastic Gradient Descent to achieve speedup. Impressive stuff! #hashingtrick #sgd
optimizerfreak 1 year ago next
@MLExpert Any benchmark comparisons with adaDelta, adaGrad, or RMSProp? I'm curious how this new algo stack up against them.
mlexpert 1 year ago next
@OptimizerFreak I don't think the paper provides benchmarks with those methods, but I suspect that could be a topic for future research. Definitely something I'd be interested in seeing.
mathlover 1 year ago prev next
This finding is a next phase improvement in the long line of theoretical innovations that began with SVD, advanced to ALS, and now has the potential to extend the bounds of feasible matrix manipulation and what it means for big data.
matrixalgouser 1 year ago next
@MathLover That's a true assessment. As hardware and software advancements progress, the applications of such algorithms to practical problems become more powerful.
gpupowered 1 year ago prev next
Nvidia must be happy. This is a perfect chance for them to argue we need more GPU support for algo devs. #lovemygpu #gpus4all
matrixalgouser 1 year ago next
@GPUpowered Absolutely! When algorithms like this are being developed, faster, more efficient hardware like GPUs will only help to unlock their full potential.
aiadvocate 1 year ago prev next
This is a huge leap for AI and ML. Just imagine the improvement in areas ranging from image and speech recognition to NLP and self-driving cars!
matrixalgouser 1 year ago next
@AIAdvocate Agree! The speed and scale of the algo have a direct impact on real-world performance, especially for AI, ML, and DL tasks.
newbiequerier 1 year ago prev next
Can this algo be used for low-rank matrix approximation? Is there a PYTHON implementation I could use for my research?
matrixalgouser 1 year ago next
@NewbieQuerier Yes, this algo is suited for low-rank matrix factorization. I haven't come across a python implementation yet, but I would look into using Julia, the language the authors used. #julialinguage
algorithmenthusiast 1 year ago prev next
This could drive a new generation of research in matrix algebra, sparse coding, and latent factor modeling!
matrixalgouser 1 year ago next
@AlgorithmEnthusiast Absolutely. Large-scale matrix factorization is only a means to an end, and this improved algo can potentially unlock whole new branches of research.