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vault backup: 2025-04-01 13:01:58

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Dane Sabo 2025-04-01 13:01:58 -04:00
parent e97bcb3b9b
commit d9510470cd
2 changed files with 27 additions and 6 deletions
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14
.sessions/Thesis_Notes.vim
View File

@ -3,7 +3,7 @@ let s:so_save = &g:so | let s:siso_save = &g:siso | setg so=0 siso=0 | setl so=-
let v:this_session=expand("<sfile>:p")
silent only
silent tabonly
cd ~/Documents/Dane\'s\ Vault/9999\ Personal/Journal
cd ~/Documents/Dane\'s\ Vault/5\ Thesis
if expand('%') == '' && !&modified && line('$') <= 1 && getline(1) == ''
let s:wipebuf = bufnr('%')
endif
@ -13,11 +13,13 @@ if &shortmess =~ 'A'
else
set shortmess=aoO
endif
badd +16 ~/Documents/Dane\'s\ Vault/5\ Thesis/4\ Meetings/DGC_DAS_328.md
badd +16 4\ Meetings/DGC_DAS_328.md
badd +32 5\ Resources/Literature_Reviews/A\ Review\ of\ Formal\ Methods\ applied\ to\ Machine\ Learning.md
argglobal
%argdel
edit ~/Documents/Dane\'s\ Vault/5\ Thesis/4\ Meetings/DGC_DAS_328.md
edit 5\ Resources/Literature_Reviews/A\ Review\ of\ Formal\ Methods\ applied\ to\ Machine\ Learning.md
argglobal
balt 4\ Meetings/DGC_DAS_328.md
setlocal fdm=manual
setlocal fde=0
setlocal fmr={{{,}}}
@ -28,12 +30,12 @@ setlocal fdn=20
setlocal fen
silent! normal! zE
let &fdl = &fdl
let s:l = 16 - ((15 * winheight(0) + 30) / 60)
let s:l = 11 - ((10 * winheight(0) + 27) / 55)
if s:l < 1 | let s:l = 1 | endif
keepjumps exe s:l
normal! zt
keepjumps 16
normal! 080|
keepjumps 11
normal! 058|
tabnext 1
if exists('s:wipebuf') && len(win_findbuf(s:wipebuf)) == 0 && getbufvar(s:wipebuf, '&buftype') isnot# 'terminal'
silent exe 'bwipe ' . s:wipebuf

19
5 Thesis/5 Resources/Literature_Reviews/A Review of Formal Methods applied to Machine Learning.md
View File

@ -11,4 +11,23 @@ This review seems to talk a good bit about that.
than other types of programming. Things such as computer vision and decision
making are very difficult to program manually relative to their ML counterpart.
- Abstract interpretation can connect different formal methods that otherwise
would seem disjoint, according to these authors.
- They spend a lot of pages (~15) on *formal methods for neural networks*.
There are two kinds.
- **Complete Formal Methods** are sound and complete, and can provide
counterexamples where appropriate. That being said, they are not usually
sound on floating point arithmetic and often neglect rounding errors.
- **Incomplete Formal Methods** are generally able to scale to larger
neural networks, and are often sound, but generally suffer from false
positives.
- In comparison, they spend 2 pages on support vector machines and decision
trees :( What did they ever do to you man!
- They also mention that formal methods for data preparation and training are
very much works in progress.