yup. deeply regret my time as a postdoc. especially the last few years in the pandemic have been the absolute worst work experience in terms of culture and job satisfaction. it's killed the passion for research i had at the start.
the worst part is that i think this experience has completely tanked my confidence.
"The way I read papers is by first reading the abstract. Then I try to state the results and prove them myself. When I get stuck I go to the paper to see what I got wrong."
> Do people think that algebraic geometry might be thought of as the root of all geometry?
I certainly don't think of it this way.
There are many geometries and spaces that cannot be described fruitfully within the framework of algebraic geometry, much the same as how there are many functions that are not analytic.
I think we're probably thinking of the same papers. There was a competition /session at last year's neurips and there's related papers linked from their webpage.
There are ways (called heuristics) to solve some NP hard problems faster than exhaustive search.
Of course, for theoretical reasons, a heuristic is not guaranteed to be faster on all problem instances.
The general idea is to do some engineering and find a heuristic that works well for your problem. More sophisticated algorithms will try multiple heuristics at the same time (I believe OR tools has options that falll in this category). There is even research by now that aims to use ML to find good heuristics for a given family of problems (where the common characteristics of the problems might lend to a dominant heuristic).
These heuristics can still have guarantees of correctness. A good place to start is by looking up "branch and bound" algorithms, e.g., for ILP problems. The idea there is to narrow the search space while maintaining some theoretical guarantees.
(Not an expert but i did a project on this once. I'm sure someone more expert can come along and correct where I'm wrong.)
As someone who was in Europe for one of the heatwaves of recent years, and whose appartment was in one of these older buildings designed this ways with thick walls etc., I can attest that this only works up to a point. This design relies on there being cold air for the building to retain. However, in a heatwave it doesn't even get cold at night. The result is your apartment gradually heating over a period of days / weeks and turning into a sauna. It is not pleasant.
By the end of that summer I was dreaming about moving back to north america where air conditioning is much more common in rental units.
> Despite all that, what I’m hoping for is that someone will come along and tell me that I got it all wrong. “When you do it as follows, the system works: $explanation” Because, you know, I’d really like to have static typing in Python.
I want X. Y (which is designed for somthing other than X) doesn't do X. Thus, I don't like Y.
FWIW I lead with this whenever I find myself teaching lin.agl. and I agree it's the most important point of context for students entering the subject (and presumably embarking on undergrad math).
Ditto. More broadly, I am bored by efforts to rehash the same introductory material from whatever your given technical topic is (math, programming, machine learning). There's already really good books out there on these things that have been written by masters and do a much better job than blogs like this (provided you read them properly).
To people out there writing educational blogs: do more research and find good, well written, timeless resources to point people to for the basics. Spend your energy writing something new that we haven't all already read.
> Perhaps I was wrong to expect mention of explicit discsussion of spectral graph theory?
I mean, this is a link to a pdf titled "chapter 3" buried deep in the directories of someone's academic homepage. There's no context about who the author is (is it a student project from a course? Part of a dissertation? notes for a draft of a book?), except that I think I can extract their name from the URL. It's pretty hard to tell, thus, who the desired audience is or what the aim of it is.
So whether one should expect one thing or another from this random pdf... I mean, without knowing more, what is there to reasonably expect?
For instance (and I can imagine this because I've taught an undergraduate course on graph theory, among other undergrad math courses), if these are notes from a course, then the instructor may have some good, context specific reasons for focusing on some topics while not going into details on others. But, as I said above, it's really impossible to know because this has just been plunked on HN with no context, as if that is somehow valuable.
Blackwell is engaged in activist investing. Their goal is to make profit by changing how the company is run. This deck is surely part of that campaign. Thus it may contain truths, but it also serves their agenda. Best to be understood in that light.
> I do find the arguments around bad governance structures and oversight weird though, as those should have been known before they invested.
Likely they knew and saw an opportunity for their investment strategy.
The first thing to know is your goal. Your goal could be things such as: to glean core concepts and methodologies, to determine how a paper relates to another research work (such as your own), to find an answer to a specific question (or determine that the paper does not answer that question), to be able to implement what is in the paper in code, or to develop the material further in your own research.
Obviously, how you should read dense technical material will differ greatly depending on your goal.
For instance, if you are looking only for an answer to a specific question, you might prefer to do a sort of DFS on the paper. Read the intro material to get an impression of where in the paper the answer might lie, then go read those parts (in descending order of likelihood of containing the answer). A similar approach might work if you already know the area and are simply looking to see if the paper contains a particular result. Sometimes this process will still require some investment in understanding notation and terminology, but time spent doing this should be kept to a minimum.
On the other hand, if you intend to master the material and develop it further, then you might take a totally different approach where you prepare your own set of detailed notes on the paper (taking care to expand, in places where your own knowledge is lacking, techinical terms and concepts that are used in the paper but not explained using secondary references). This might also include working out many examples or thought experiments to help you internalize the logic and concepts. For a significant paper this process will take a long time, but it will be worthwhile if you truely want to understand the paper comprehensively and be able to build on it in a substantial way.
No matter your goal and approach, the point is that reading a paper productively is a systematic thing. Skimming technical papers lacksidasically is often a waste of time that doesn't produce value.
And this makes everything else in the survey easier. Topic sentences and presentation skills are useful but most important is having something original and substantial to say. The rest follows and is easy by comparison.
the worst part is that i think this experience has completely tanked my confidence.
now id take a jr python dev job in a second.