Best paper awards (since 1996) for top-tier computer science conferences: AAAI, ACL, CHI, CIKM, CVPR, FOCS, FSE, ICCV, ICML, ICSE, IJCAI, INFOCOM, KDD, MOBICOM, NSDI, OSDI, PLDI, PODS, S&P, SIGCOMM, SIGIR, SIGMETRICS, SIGMOD, SODA, SOSP, STOC, UIST, VLDB, WWW.
Source: Best paper awards at AAAI, ACL, CHI, CIKM, CVPR, FOCS, FSE, ICCV, ICML, ICSE, IJCAI, INFOCOM, KDD, MOBICOM, NSDI, OSDI, PLDI, PODS, S&P, SIGCOMM, SIGIR, SIGMETRICS, SIGMOD, SODA, SOSP, STOC, UIST, VLDB, WWW
Reproducible machine learning with PyTorch and Quilt
Source: Reproducible machine learning with PyTorch and Quilt
Backblaze Durability is 99.999999999% — And Why It Doesn’t Matter
Source: Reliability in Cloud Computing: What Do All Those 9s Mean?
It’s been two years since I wrote #define CTO, in which I documented my quest for a role where I could have scalable impact by writing code. I’ve finally found that role, though not by seeking it — instead, I sought out a problem more important to me than my role within it, brought together the right people, and found that I can best make them effective by writing code.
Source: #define CTO OpenAI
Being a pragmatist, I recognize that—like the eternal spaces vs tabs debate—one’s branching and merging styles are often held dear. If I may ask you to set all that aside for just a few minutes though, have a look and see if any of the following might make your environment more productive. The next caveat is that this article is focused on Git, which is a distributed version control system (DVCS). The conclusions may or may not be relevant to other DVCS’s but probably not to centralized version control systems, like Subversion, where branches can be expensive.
Git Strategizing: Branch, Commit, Review, and Merge
Source: Git Strategizing: Branch, Commit, Review, and Merge – Simple Talk
||Hi everyone,I work at a university institute of computational biology. Besides doing research, we also teach quite a few courses for biology students, many of which include an introduction to programming (mostly with R, but also Python). We have a long-standing debate as to what the best approach to this is, and I would like to hear some of your opinions on the matter.
Basically, we have two factions: the “tools-first” and the “fundamentals-first” approach. The supporters of “tools-first” argue that we are teaching biologists, not software developers. They like to teach the specific tools (languages, libraries, functions, etc.) that our students are actually going to need as quickly as possible. To cover as much ground as possible, they are willing to sacrifice a deeper understanding of programming.
Ask HN: Teaching programming in computational biology?
Source: Ask HN: Teaching programming in computational biology? | Hacker News