Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
文献紹介 / Knowledge Tracing with GNN
Search
Atom
December 04, 2020
0
100
文献紹介 / Knowledge Tracing with GNN
文献紹介と書いてあるが自分の用のメモ
公開しなくても良いかなと思ったが公開
Atom
December 04, 2020
Tweet
Share
More Decks by Atom
See All by Atom
文献紹介 / Structure-based Knowledge Tracing: An Influence Propagation View
roraidolaurent
0
100
文献紹介 / Non-Intrusive Parametric Reduced Order Models withHigh-Dimensional Inputs via Gradient-Free Active Subspace
roraidolaurent
0
62
ニューラルネットワークのベイズ推論 / Bayesian inference of neural networks
roraidolaurent
2
2.8k
Graph Convolutional Networks
roraidolaurent
0
250
文献紹介 / A Probabilistic Annotation Model for Crowdsourcing Coreference
roraidolaurent
0
79
文献紹介Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
roraidolaurent
0
120
文献紹介/ Bayesian Learning for Neural Dependency Parsing
roraidolaurent
0
130
ポッキー数列の加法定理 / Pocky number additon theorem
roraidolaurent
0
250
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
roraidolaurent
1
170
Featured
See All Featured
SEOcharity - Dark patterns in SEO and UX: How to avoid them and build a more ethical web
sarafernandez
0
130
Agile Leadership in an Agile Organization
kimpetersen
PRO
0
95
Mind Mapping
helmedeiros
PRO
1
100
The Mindset for Success: Future Career Progression
greggifford
PRO
0
250
Making the Leap to Tech Lead
cromwellryan
135
9.7k
New Earth Scene 8
popppiees
1
1.6k
From Legacy to Launchpad: Building Startup-Ready Communities
dugsong
0
160
What’s in a name? Adding method to the madness
productmarketing
PRO
24
3.9k
Code Review Best Practice
trishagee
74
20k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.3k
Technical Leadership for Architectural Decision Making
baasie
2
270
The Spectacular Lies of Maps
axbom
PRO
1
570
Transcript
None
None
∈ , ∈ {0,1}2 ∈ {0,1} ∈ {0,1} + 1
≡ +1 = , , , = 1 , ⋯ , ⊆ × , ∈ ℝ× ∈ ∈ ℝ
None
∈ ℝ2× ∈ ℝ× () ∈ ℝ ∈
, ℎ
None
None
None
None
None
None
☓
None
None
∈ {0,1}
None
None
None
−1 から問題 (スキル をもつ)に正答確率を アテンションで求めるが, と同じスキルをもつ問題(例 )
の正誤情報 は −1 では失われている可能性が大きい. に関連する問題を選択し, その情報 についても アテンションをとる.
None
None
None
None
None
None
None