2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/Boltzmann-Machines.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-boltzmann-machines	Boltzmann Machines	10_Wiki/Topics	verified	self	볼츠만 머신 RBM restricted Boltzmann machine deep belief network energy-based model contrastive divergence	none	A	0.88	applied	boltzmann-machine rbm energy-based-model deep-learning-history hinton contrastive-divergence		2026-05-10	pending	language framework Python PyTorch / scikit-learn

Boltzmann Machines

📌 한 줄 통찰

"매 data distribution 의 energy 의 model". 매 stat mech 의 Boltzmann distribution 의 inspire. 매 deep learning 의 spark (Hinton 2006 RBM pre-training). 매 modern: 매 energy-based model (EBM) 의 의 base + 매 score matching + 매 diffusion 의 connection.

📖 핵심

매 history

• 1985: Hinton & Sejnowski의 Boltzmann Machine.
• 2002: Hinton의 Contrastive Divergence (CD) 학습.
• 2006: Hinton's "A Fast Learning Algorithm for Deep Belief Networks" — 매 deep learning 의 부활.
• 2007-2012: 매 pre-training 의 ImageNet 의 unleash.
• 2010s: 매 backprop + ReLU + dropout 의 supersede.
• 2020s: 매 energy-based model 의 revival (Du, LeCun).

매 architecture

Vanilla Boltzmann Machine

• 매 모든 매 unit 가 connected.
• 매 visible + hidden.
• 매 train 어려움 (intractable).

RBM (Restricted)

• 매 same-layer connection X.
• 매 visible ↔ hidden 만.
• 매 efficient sampling.

DBN (Deep Belief Network)

• 매 RBM 의 stack.
• 매 layer-wise pre-training.

DBM (Deep Boltzmann Machine)

• 매 모든 layer 의 bidirectional.
• 매 train 매 hard.

매 energy formulation

E(v, h) = -\sum_i a_i v_i - \sum_j b_j h_j - \sum_{i,j} v_i W_{ij} h_j P(v, h) = \frac{e^{-E(v, h)}}{Z}

• 매 Z = partition function (intractable).

매 학습: Contrastive Divergence (CD-k)

1. 매 data v0.
2. 매 sample h0 from P(h | v0).
3. 매 sample v1 from P(v | h0). [k step 의 Gibbs]
4. 매 update: ΔW = lr * (v0 h0^T - v1 h1^T).

매 modern relevance

• Energy-Based Model (EBM): 매 LeCun 의 advocate.
• Score matching: 매 gradient 의 학습 — 매 diffusion model 의 base.
• Diffusion model (DDPM): 매 EBM 의 변형.
• GAN: 매 implicit EBM.
• JEM (Joint Energy Model): 매 classifier 의 EBM 의 reframe.

매 modern application

• Anomaly detection: 매 low energy = normal.
• Generative model (legacy): 매 collaborative filtering.
• Recommender (Netflix prize 의 RBM).
• Pre-training (legacy, mostly replaced).
• Quantum Boltzmann (quantum computing).

매 vs modern alternative

측면	RBM	Modern
Density estimation	weak	Diffusion / Flow
Pre-training	weak	Self-supervised
Generation	OK	GAN / Diffusion
Tractability	hard	tractable (specific)

→ 매 historical importance > 매 current usage.

💻 패턴

RBM (scikit-learn)

from sklearn.neural_network import BernoulliRBM
from sklearn.datasets import load_digits

X = load_digits().data / 16.0  # 매 normalize

rbm = BernoulliRBM(n_components=64, learning_rate=0.06, n_iter=20)
rbm.fit(X)

# 매 reconstruction
import numpy as np
hidden = rbm.transform(X[:1])  # 매 hidden activations
print(hidden.shape)  # (1, 64)

RBM (PyTorch from scratch)

import torch
import torch.nn as nn

class RBM(nn.Module):
    def __init__(self, n_visible, n_hidden):
        super().__init__()
        self.W = nn.Parameter(torch.randn(n_hidden, n_visible) * 0.01)
        self.v_bias = nn.Parameter(torch.zeros(n_visible))
        self.h_bias = nn.Parameter(torch.zeros(n_hidden))
    
    def sample_h(self, v):
        p_h = torch.sigmoid(F.linear(v, self.W, self.h_bias))
        return p_h, torch.bernoulli(p_h)
    
    def sample_v(self, h):
        p_v = torch.sigmoid(F.linear(h, self.W.t(), self.v_bias))
        return p_v, torch.bernoulli(p_v)
    
    def free_energy(self, v):
        wx_b = F.linear(v, self.W, self.h_bias)
        return -torch.sum(F.softplus(wx_b), dim=1) - v @ self.v_bias

def cd_k(rbm, v0, k=1, lr=0.01):
    """매 Contrastive Divergence."""
    p_h0, h0 = rbm.sample_h(v0)
    
    vk = v0
    for _ in range(k):
        p_h, h = rbm.sample_h(vk)
        p_v, vk = rbm.sample_v(h)
    
    p_hk, hk = rbm.sample_h(vk)
    
    # 매 gradient
    rbm.W.grad = -((p_h0.t() @ v0 - p_hk.t() @ vk) / v0.size(0))
    rbm.v_bias.grad = -((v0 - vk).mean(0))
    rbm.h_bias.grad = -((p_h0 - p_hk).mean(0))

Energy-Based Model (modern)

class EBM(nn.Module):
    """매 energy F(x) = MLP."""
    def __init__(self, dim):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, 256), nn.ReLU(),
            nn.Linear(256, 256), nn.ReLU(),
            nn.Linear(256, 1),
        )
    
    def energy(self, x):
        return self.net(x).squeeze(-1)

def langevin_sample(ebm, x, n_steps=100, step_size=0.1, noise=0.01):
    """매 Langevin dynamics 의 EBM 의 sample."""
    x = x.detach().requires_grad_()
    for _ in range(n_steps):
        e = ebm.energy(x).sum()
        grad = torch.autograd.grad(e, x)[0]
        x = x - step_size * grad + noise * torch.randn_like(x)
        x = x.detach().requires_grad_()
    return x

Diffusion model (related EBM)

# 매 DDPM 의 sketch — 매 noise 의 add + reverse
def diffusion_train(model, x0, T=1000):
    t = torch.randint(0, T, (x0.size(0),))
    noise = torch.randn_like(x0)
    alpha_bar = noise_schedule[t]
    xt = torch.sqrt(alpha_bar) * x0 + torch.sqrt(1 - alpha_bar) * noise
    
    pred_noise = model(xt, t)
    return F.mse_loss(pred_noise, noise)

Anomaly detection (EBM)

def is_anomaly(ebm, x, threshold):
    """매 high energy = 매 unusual."""
    return ebm.energy(x).item() > threshold

🤔 결정 기준

상황	Approach
Modern generative	Diffusion / GAN
Anomaly detection	EBM / Autoencoder
Historical study	RBM / DBN
Quantum	Quantum Boltzmann
Pre-training	Self-supervised (BERT, MAE)
Sparse coding	Sparse autoencoder

기본값: 매 historical 의 understand 가, 매 production 의 모더 매 alternative.

🔗 Graph

• 부모: Generative-AI
• 변형: RBM
• 응용: Diffusion-Model · Generative-Adversarial-Networks · Anomaly-Detection
• Adjacent: Contrastive-Divergence · Auto-Encoding · Bayesian-Brain-Hypothesis

🤖 LLM 활용

언제: 매 deep learning history. 매 EBM 의 understand. 매 anomaly detection. 매 diffusion 의 connection. 언제 X: 매 production generative (use diffusion). 매 production pre-train (use SSL).

❌ 안티패턴

• RBM 의 production 의 expect: 매 outdated.
• Pre-training 의 RBM 으로 의 modern (BERT 의 era): 매 use SSL.
• Z (partition) 의 compute attempt: 매 intractable.
• Single-step CD: 매 biased estimator.
• Continuous data 의 binary RBM: 매 wrong.

🧪 검증 / 중복

• Verified (Hinton 2002 CD, 2006 DBN paper).
• 신뢰도 A.
• Related: Diffusion-Model · Energy-Based-Models · Auto-Encoding · Self-Supervised-Learning.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — RBM + EBM + diffusion connection + 매 PyTorch / sklearn code