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pandas-ml

pandas, scikit-learn, xgboost and seaborn integration

paper-notebook2013

A paper about the IPython Notebook written in 2013

paper2gui

Convert AI papers to GUI，Make it easy and convenient for everyone to use artificial intelligence technology。让每个人都简单方便的使用前沿人工智能技术

paperweekly_bot

a bot for paperweekly

paragraphranker

Implementation of Paragraph Ranker for Open-Domain QA

parallel-gbdt

Parallel Gradient Boosting Decision Trees

parallel-nlp-tokenization

A parallel NLP Tokenization tool for transformers

parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning

parallel_trainers

Parallel trainers using the python binding for MPI

parallelsparsematrixfactorization

Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.

paraphrase_generation

The latent bag of words model for paraphrase generation

paraphrase_identification

Examine two sentences and determine whether they have the same meaning.

paraphrasing-squad

Datasets for the paper "Improving the Robustness of Question Answering Systems to Question Paraphrasing" (ACL 2019)

parg

parlai

A framework for training and evaluating AI models on a variety of openly available dialog datasets.

parsetron

A natural language semantic parser

parsetron-tutorial

Tutorial materials for parsetron documentation

partners_obesity_subjective_feature_learning

passage_select

CCIR code\ MRC task

path2vec

Learning to represent shortest paths and other graph-based measures of node similarities with graph embeddings

pathnet

PathNet model for Multi-hop Reading Comprehension (https://arxiv.org/pdf/1811.01127.pdf)

patsy

Describing statistical models in Python using symbolic formulas

pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

paws

This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification.

pbl_15_multilabeltextclassification

pca-svd-movies

Eigenvector decomposition and Singular Value Decomposition Implementation, applied to predicting movie ratings (Netflix problem)

pdfcpu

A PDF processor written in Go.

person2vec

persona-dialogue-generation

The code of ACL 2020 paper "You Impress Me: Dialogue Generation via Mutual Persona Perception"

pet

This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"