"Paper"? There are lots of pure/applied math journals packed with papers. I touched on the fields of statistics, probability, optimization, and stochastic processes, and each of these fields has their own journals.
Usually a start better than papers in journals is books. A first list of books would be for a good ugrad pure math major. There get to concentrate on analysis, algebra, geometry with some concentration on topology or foundations.
For grad school might want to do well with measure theory, functional analysis, probability based on measure theory, statistics based on that probability, optimization, stochastic processes, numerical analysis, pure/applied algebra (applied algebra -- coding theory), etc.
Then, sure, work with some promising applications and then dig deeper into relevant fields as needed by the applications.
One key to success is good "problem selection". So, with good problem selection, some good background, and maybe some original work, might do really well on a good problem, publish some papers, do a good startup, make some big bucks, etc. That's what I'm working on -- picked my problem, for the first good, an excellent, solution did some original applied math derivations, have my production code in alpha test, 24,000 programming language statements in 100,000 lines of typing.
It's applied math; hopefully it's valuable; but I wouldn't call it either AI or ML.
In case my view is not obvious, it is that the best help for the future of computing is pure/applied math and not much like current computer science. Computer science could help -- just learn and do more pure/applied math.
Usually a start better than papers in journals is books. A first list of books would be for a good ugrad pure math major. There get to concentrate on analysis, algebra, geometry with some concentration on topology or foundations.
For grad school might want to do well with measure theory, functional analysis, probability based on measure theory, statistics based on that probability, optimization, stochastic processes, numerical analysis, pure/applied algebra (applied algebra -- coding theory), etc.
Then, sure, work with some promising applications and then dig deeper into relevant fields as needed by the applications.
One key to success is good "problem selection". So, with good problem selection, some good background, and maybe some original work, might do really well on a good problem, publish some papers, do a good startup, make some big bucks, etc. That's what I'm working on -- picked my problem, for the first good, an excellent, solution did some original applied math derivations, have my production code in alpha test, 24,000 programming language statements in 100,000 lines of typing.
It's applied math; hopefully it's valuable; but I wouldn't call it either AI or ML.
In case my view is not obvious, it is that the best help for the future of computing is pure/applied math and not much like current computer science. Computer science could help -- just learn and do more pure/applied math.