OCTIS: Comparing and Optimizing Topic models is Simple!

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics... (read more)

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Methods used in the Paper


METHOD TYPE
Dense Connections
Feedforward Networks
Scaled Dot-Product Attention
Attention Mechanisms
Softmax
Output Functions
GELU
Activation Functions
Linear Warmup With Linear Decay
Learning Rate Schedules
WordPiece
Subword Segmentation
Attention Dropout
Regularization
Layer Normalization
Normalization
Residual Connection
Skip Connections
Weight Decay
Regularization
Multi-Head Attention
Attention Modules
Dropout
Regularization
Adam
Stochastic Optimization
BERT
Language Models
VAE
Generative Models
Contextualized Topic Models
Topic Embeddings
LDA
Dimensionality Reduction