Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
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Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#2The paper's references have some good ones for getting more acquainted with these subjects; this one being a nice dense one to start with:
- Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges: https://geometricdeeplearning.com/
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#3Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success on that plan.
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#4Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success o…
I believe a use-case(s) receiving attention is drug design, protein design, chemical design, etc.
Here is a summer school by the London Geometry and Machine Learning group where research topics are shared and discussed. - https://www.logml.ai/
Here is another group, a weekly reading group on graphs and geometry: https://portal.valencelabs.com/logg
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#5Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success o…
I believe a use-case(s) receiving attention is drug design, protein design, chemical design, etc. Here is a summer school by the London Geometry and Machine Learning group where research topics are shared and discussed. - https://www.logml.ai/ Here is another group, a weekly reading group on graphs and geometry: https://portal.valencelabs.com/logg
Thanks. That's certainly very interesting. Albeit it seems to me that the number of jobs doing geometric and topological ML/AI work in the drug or protein design space would be quite limited, because any discovery ultimately has to be validated through a wet lab process (or perhaps phase 1-3 clinical trials for drugs) which is expensive and time-consuming. However, I'm very uninformed and perhaps there is indeed a sizable job market here.
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#6Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success o…
I believe a use-case(s) receiving attention is drug design, protein design, chemical design, etc. Here is a summer school by the London Geometry and Machine Learning group where research topics are shared and discussed. - https://www.logml.ai/ Here is another group, a weekly reading group on graphs and geometry: https://portal.valencelabs.com/logg
As someone who did an applied math PhD before drifting towards ML, it's worth pointing out that these applied math groups typically talk about applications, but the real question is whether they are actually used for the stated application in practice due to outperforming methods that use less pretty math. Typically (in every case i have seen) the answer is "no", and the mathematicians don't even really care about solving the applied problems nor fully understand what it would mean to do so. It's just a source of grant-justifiable abstract problems.
I would love to be proven wrong though!
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#7Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success o…
I've had some success using hyperbolic embeddings for bert like models.
It's not something that the companies I've worked for advertised or wrote papers about.
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#8One common theme I see in the paper(e.g. in protein folding) is:
"Identify what properties are important (geometry, algebra, topo) and which one is an useful prior and then "use" the guide to select an initial struct. This is probably harder than it sounds(unlike bayesian priors which are more forgiving for one to select, but quite like them in that they both require special assumptions)."
I wonder: could one use it to bring together certain multimodal data and a proposed network for a task? Like could one bring in sensor, map topology, urban topology, pictures which have certain properties and that help me use this guide to make a statement like : "Street data could be embedded with Sensor data to do ABC kind of inference using XYZ NNetwork structure because this paper suggests that is a reasonable thing to do"?
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#9Is geometric, topological, and algebraic ML/data analysis actually used in the industry? It is certainly beautiful math. However, during grad school I met a few pure math PhD students who were saying that after finishing their PhD they will just go into industry to do topological data analysis (this was about 10 years ago and ML wasn't yet as hyped up). However, I have never heard of anybody actually having success o…
I don't think there's much use currently. But I kinda like the direction of the paper anyway. Most mathematical objects in ML have geometric or topological structure, implicitly defined. By making that structure explicit, we at worst have a fresh new perspective on some ML thing. Like how viewing the complex numbers on a 2d cartesian plane often clicks more for students compared to the dry algebraic perspective. So even in the worst case I think there's some pedagogical clarity here.
Re: Guide to Machine Learning with Geometric, Topological, and Algebraic Structures
#10Earlier quoted context omitted.
I believe a use-case(s) receiving attention is drug design, protein design, chemical design, etc. Here is a summer school by the London Geometry and Machine Learning group where research topics are shared and discussed. - https://www.logml.ai/ Here is another group, a weekly reading group on graphs and geometry: https://portal.valencelabs.com/logg
Thanks. That's certainly very interesting. Albeit it seems to me that the number of jobs doing geometric and topological ML/AI work in the drug or protein design space would be quite limited, because any discovery ultimately has to be validated through a wet lab process (or perhaps phase 1-3 clinical trials for drugs) which is expensive and time-consuming. However, I'm very uninformed and perhaps there is indeed a si…
I think the job market in general for this kind of stuff is "small"; but you can find jobs. Look at Isomoprhic Labs for example. There are new AI/ML companies that have emerged in recent years, helped by success of things like AlphaFold. I think your question is really: does this research actually creates tangible results? If it did, it would be able to create more jobs to support it by virtue of being economically successfully and therefore growing?