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๐ŸŒฎ
Taco

Youngsoo Kim znxkznxk1030

๐ŸŒฎ
Taco
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Sliding Window

๊ฐœ์š”

์‚ฌ์šฉ์ฒ˜

Template

n = len(nums)

Binary Search

๊ฐœ์š”

  • ์ •๋ ฌ๋œ ๋ฐฐ์—ด์—์„œ ํŠน์ • ๊ฐ’์„ ์ฐพ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜.
  • ์ค‘๊ฐ„ ๊ฐ’์„ ๊ธฐ์ค€์œผ๋กœ ํƒ์ƒ‰ ๋ฒ”์œ„๋ฅผ ์ ˆ๋ฐ˜์”ฉ ์ค„์—ฌ๊ฐ€๋ฉฐ ํšจ์œจ์ ์œผ๋กœ ํƒ์ƒ‰. ( ์‹œ๊ฐ„ ๋ณต์žก๋„: $O(logN)$ )

์‚ฌ์šฉ์ฒ˜

  1. upper bound/lower bound ์ฐพ๊ธฐ
#1.
์•ˆ๋…•ํ•˜์„ธ์š”. 10์กฐ ๋ฐœํ‘œ๋ฅผ ์‹œ์ž‘ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.
์ €ํฌ๋Š” "์Œ์„ฑ-์ด๋ฏธ์ง€ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ์„ ํ™œ์šฉํ•˜์—ฌ ์Œ์„ฑ๊ณผ ๋งค์นญ๋˜๋Š” ์ธ๋ฌผ์„ ์ถ”๋ก ํ•˜๋Š” AI ๋ชจ๋ธ"์ด๋ผ๋Š” ์ฃผ์ œ๋กœ ์„ธ๋ฏธ๋‚˜ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค.
#2.
๋จผ์ € ํ”„๋กœ์ ํŠธ ๊ฐœ์š”๋ฅผ ์„ค๋ช…๋“œ๋ฆฐ ํ›„, ๋‚ด์šฉ๊ณผ ์‘์šฉ, ์ผ์ • ์ˆœ์œผ๋กœ ๋ฐœํ‘œ๋ฅผ ์ง„ํ–‰ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.
#3.

๊ธฐ๊ณ„ํ•™์Šต๊ณผ์ •๋ณด์ด๋ก  - ๊ณผ์ œ2 ๋…ผ๋ฌธ ์ดˆ์•ˆ ๊ฐœ์„ ํ•˜๊ธฐ

2025451021
์ธ๊ณต์ง€๋Šฅํ•™๊ณผ
๊น€์˜์ˆ˜

Title

On the Effect of Negative-Pair Variance in Contrastive Learning and a VRN-Based Solution

์ค‘๊ฐ„๊ณ ์‚ฌ ์˜ˆ์ƒ ๋ฌธ์ œ

1. (Finite) Markov Decision Process

1. ๊ฐ•ํ™”ํ•™์Šต(Reinforcement Learning)์˜ ์ •์˜๋ฅผ ์„œ์ˆ ํ•˜๊ณ , ์ง€๋„ํ•™์Šต(Supervised Learning)๊ณผ์˜ ์ฐจ์ด์ ์„ ์˜ˆ์‹œ์™€ ํ•จ๊ป˜ ์„ค๋ช…ํ•˜์‹œ์˜ค

A goal-directed learning from interaction

์ค‘๊ฐ„๊ณ ์‚ฌ ์˜ˆ์ƒ ๋ฌธ์ œ

Introduction

1. ๋‹ค์Œ ๊ฐœ๋…๋“ค: ์ธ๊ณต์ง€๋Šฅ(AI), ๋จธ์‹ ๋Ÿฌ๋‹(ML), ๋”ฅ๋Ÿฌ๋‹ (DL)์˜ ๊ด€๊ณ„๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ฐ๊ฐ ๋Œ€ํ‘œ์ ์ธ ์˜ˆ์‹œ๋ฅผ ํ•˜๋‚˜์”ฉ ๋“ค์–ด ์„œ์ˆ ํ•˜์‹œ์˜ค

$$ ๋”ฅ๋Ÿฌ๋‹ \subset ๋จธ์‹ ๋Ÿฌ๋‹ \subset ์ธ๊ณต์ง€๋Šฅ$$3

์ธ๊ณต์ง€๋Šฅ์€ ์ธ๊ฐ„์ฒ˜๋Ÿผ ์‚ฌ๊ณ ํ•˜๊ณ  ํ–‰๋™ํ•˜๋Š” ๊ธฐ๊ณ„๋ฅผ ๋งŒ๋“œ๋Š” ๊ธฐ์ˆ  ์ „๋ฐ˜์„ ์˜๋ฏธํ•œ๋‹ค.

๊ฐ•์˜ ๋‚ด์šฉ ์š”์•ฝ ๊ณผ์ œ

2025451021
์ธ๊ณต์ง€๋Šฅํ•™๊ณผ
๊น€์˜์ˆ˜

1. Entropy ์ •์˜

Entropy๋ž€ ์–ด๋–ค ํ™•๋ฅ  ๋ณ€์ˆ˜์— ๋Œ€ํ•ด ์ •๋ณด์˜ ์–‘์„ ์ธก์ •ํ•˜๋Š” ๊ฐœ๋…์ด๋‹ค. ์—ฌ๊ธฐ์—์„œ ์ •๋ณด๋Š” ๋ถˆํ™•์‹ค์„ฑ์„ ์˜๋ฏธํ•˜๊ณ  ํ•ด๋‹น ํ™•๋ฅ  ๋ณ€์ˆ˜์˜ ๋ถˆํ™•์‹ค์„ฑ์˜ ์ •๋„๋ฅผ ์˜๋ฏธํ•œ๋‹ค.

import random
import numpy as np
from visualize_train import draw_value_image, draw_policy_image
# left, right, up, down
ACTIONS = [np.array([0, -1]),
np.array([0, 1]),
np.array([-1, 0]),
import numpy as np
from numpy.linalg import inv
from visualize_train import draw_value_image, draw_policy_image
# left, right, up, down
ACTIONS = [np.array([0, -1]),
np.array([0, 1]),
np.array([-1, 0]),
np.array([1, 0])]