ML Study

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ML Study

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finance

JS

ML

Gen Model

3D Gaussian Splatting

Auto Regressive Model

classifier-free guidance

Density estimation

Generative Model

Latent Variable Model

stable diffusion

general

batch normalization

Data Augmentation

hyper connection

Layer Normalization

reparametrization trick

skip connection

LLM

context

continual learning

nested learning

RL

bellman equation

double q-learning

policy gradient

Policy Improvement Theorem

segmentation

theory

Local minimum 문제가 적은 이유

manifold hypothesis

Out-Of-Distribution

Permutation Symmetry

train

natural gradient

vision

swin transformer

경량화

structured pruning

unstructured pruning

4D Gaussian Splatting

Continuous Symmetry

Cross Entropy Loss

Gradient Descent

Linear Attention

Linear Regression

multi head attention

Positional encoding

Supervised Learning

Unsupervised Learning

Variational Inference

Python

subprocess handle

제너레이터

개발

Intermediate Representation

Tailwind breakpoint

기초 cs

c

수학

선형대수

doubly stochastic matrix

linear subspace

orthogonal matrix

projection matrix

Rank-One matrix

sinkhorn-knopp argorithm

Spectral Decomposition

subspace coding

해석학

stiefel manifold

fourier transform

inverse fourier transform

정보 이론

Mutual Information

하드웨어

확률-통계

Gaussian Distribution

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확률변수

$X : Ω \to S$

Ω: 전체 샘플 집합 (표본공간).
S⊂R: 나올 수 있는 값들의 집합 (상태공간)

표본공간에서 상태공간으로 연결하는 함수다.

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