Probabilistic Reasoning

Factorie (Scala)
Figaro (Scala)
PyMC4 (Python)
PyMC3 (Python)
Probability (Python)
BayesLoop (Python)
Tweety (Java)
Dimple (Java)
Chimple (Java)
WebPPL (JavaScript)

Probabilistic Programming and Bayesian Methods for Hackers
The Design and Implementation of Probabilistic Programming Languages

Natural Computation

• Cellular Automata
• Evolutionary Computation
• Swarm Intelligence
• Artificial Immune Systems
• Artificial Life
• Quantum Computing
• Systems Biology
• Synthetic Biology
• Cellular Computing
• DNA Computing
• Amorphous Computing
• Membrane Computing
• Neural Computation

Global Optimization

Essentials of Metaheuristics

Conversational Dialogs

• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
• Neural Responding Machine for Short-Text Conversation
• A Neural Conversation Model

Machine Translation

• Sequence to Sequence Learning with Neural Networks
• Neural Machine Translation by Jointly Learning to Align and Translate
• A Convolutional Encoder Model for Neural Machine Translation
• Convolutional Sequence to Sequence Learning
• Convolutional Over Recurrent Encoder for Neural Machine Translation
• Neural Machine Translation
• OpenNMT

Mining Knowledge Graphs from Text

Mining Knowledge Graphs from Text

Entity Linking and Disambiguation

• Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
• Deep Joint Entity Disambiguation with Local Neural Attention
• Joint Learning of the Embedding of Words and Entities for NE Disambiguation
• A Survey of Named Entity Recognition and Classification
• Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web
• Knowledge Base Population: Successful Approaches and Challenges
• SpeedRead: A Fast Named Entity Recognition Pipeline
• Capturing Semantic Similarity for Entity Linking with CNNs
• On the Effectiveness of Feature Set Augmentation Using Clusters of Word Embeddings
• Bi-Directional LSTM-CRF Models for Sequence Tagging
• Neural Architectures for Named Entity Recognition
• End-to-End Sequence Labeling via Bi-Directional LSTM-CNNs-CRF

Natural Language Understanding

• A Hierarchical Neural Autoencoder for Paragraphs & Documents
• LSTM Over Tree Structures
• Low-Dimensional Embeddings of Logic
• Markov Logic Networks
• A Neural Probabilistic Language Model
• Retrofitting Word Vectors to Semantic Lexicons
• Unsupervised Learning of the Morphology of Natural Language
• Computational Grounded Cognition
• Statistical Language Models Based On Neural Networks
• Understanding Natural Language Understanding
• NLU at Edinburgh
• Deep Learning for Comprehension

Natural Language Generation

• Survey of Natural Language Generation
• Generating Sentences from Semantic Vector Representations
• Deep Learning for NLG
• Generating Text using RNNs
• Text Generator Guide
• NLG Components Add Value
• Data-to-Document
• NLG System Tools
• NLG vs Templates
• Challenges of surface realisation
• SIGGen
• An ethical checklist for robot journalism
• Automation in the newsroom
• Seq2SQL
• Learning to Write
• New Generalization
• NLG at Edinburgh

Generative Models

• Hidden Markov Model
• Gaussian Mixture Model
• Naive Bayes
• Latent Dirichlet Allocation
• Restricted Boltzmann Machines
• Generative Adversarial Networks
• Variational Autoencoder
• Probabilistic Context Free Grammar
• Generative Long-Short-Term-Memory
• Helmholtz Machine

Open Neural Network Exchange

ONNX

Markov Chain Monte Carlo Sampling

• Metropolis-Hastings
• Gibbs Sampling
• Slice Sampling
• Reversible-Jump
• Multiple-Try Metropolis
• Langevin Rule
• Hamiltonian
• Simulated Tempering

Demand Forecasting

One can utilize the various macro-environmental factors to evaluate demand forecasting. The below list the various types. However, they are invariably grouped under PEST, PESTEL, PESTLE, SLEPT, STEPE, STEEPLE, STEEPLED, DESTEP, SPELIT, STEER. B2B market places tend to be affected more by social factors. Defense contractors tend to be affected by political factors. Factors that are more frequent or volatile may have higher importance. Conglomerates may tend to divide factors by departmental assessment or even specific to a geographical location. One can use these models to connect with micro-environmental and internal factors. Additionally, SWOT analysis may also be used: Strength, Weakness, Opportunities, Threats.

• Political
• Social
• Economic
• Technological
• Legal
• Environmental
• Demographics
• Regulatory
• Inter-cultural
• Ethical
• Educational
• Physical
• Religious
• Security
• Competition
• Ecological
• Geographical
• Historical
• Organizational
• Temporal