Allocation de Dirichlet latente

Dans le domaine du traitement automatique des langues, l’allocation de Dirichlet latente (de l’anglais Latent Dirichlet Allocation) ou LDA est un modèle génératif probabiliste permettant d’expliquer des ensembles d’observations, par le moyen de groupes non observés, eux-mêmes définis par des similarités de données. Thèmes en LDA Par exemple, si les observations (\beta) sont les mots collectés dans un ensemble de documents textuels (M), le modèle LDA suppose que chaque document (M) est un mélange (\theta) d’un petit nombre de sujets ou thèmes (\alpha topics), et que la génération de chaque occurrence d’un mot (w) est attribuable (probabilité) à l’un des thèmes (t) du document. Le modèle LDA est un exemple de « modèle de sujet » . Il a d'abord été présenté comme un modèle graphique pour la détection de thématiques d’un document, par David Blei, Andrew Ng et Michael Jordan en . Les app

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

A Probabilistic Approach to Socio-Geographic Reality Mining

Katayoun Farrahi

As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society. For example, by understanding a community's movements and interactions, appropriate measures may be put in place to prevent the threat of an epidemic. The study of such human-centric massive datasets requires advanced mathematical models and tools. In this thesis, we investigate probabilistic topic models as unsupervised machine learning tools for large-scale socio-geographic activity mining. We first investigate two types of probabilistic topic models for large-scale location-driven phone data mining. We propose a methodology based on Latent Dirichlet Allocation, followed by the Author Topic Model, for the discovery of dominant location routines mined from the MIT Reality Mining data set containing the activities of 97 individuals over the course of a 16 month period. We investigate the many possibilities of our proposed approach in terms of activity modeling, including differentiating users with high and low varying lifestyles and determining when a user's activities fluctuate from the norm over time. We then consider both location and interaction features from cell tower connections and Bluetooth, in single and multimodal forms for routine discovery, where the daily routines discovered contain information about the interactions of the day in addition to the locations visited. We also propose a method for the prediction of missing multimodal data based on Latent Dirichlet Allocation. We further consider a supervised approach for day type and student type classification using similar socio-geographic features. We then propose two new probabilistic approaches to alleviate some of the limitations of Latent Dirichlet Allocation for activity modeling. Large duration activities and varying time duration activities can not be modeled with the initially proposed methods due to problems with input and model parameter size explosion. We first propose a Multi-Level Topic Model as a method to incorporate multiple time duration sequences into a probabilistic generative topic model. We then propose the Pairwise-Distance Topic Model as an approach to address the problem of modeling long duration activities with topics. Finally, we consider an application of our work to the study of influencing factors in human opinion change with mobile sensor data. We consider the Social Evolution Project Reality Mining dataset, and investigate other mobile phone sensor features including communication logs. We consider the difference in behaviors of individuals who change political opinion and those who do not. We combine several types of data to form multimodal exposure features, which express the exposure of individuals to others' political opinions. We use the previously defined methodology based on Latent Dirichlet Allocation to define each group's behaviors in terms of their exposure to opinions, and determine statistically significant features which differentiate those who change opinions and those who do not. We also consider the difference in exposure features of individuals that increases their interest in politics versus those who do not. Overall, this thesis addresses several important issues in the recent body of work called Computational Social Science. Investigations principled on mathematical models and multiple types of mobile phone sensor data are performed to mine real life human activities in large-scale scenarios.

EPFL2011

DeepSuccess - Predict the Success of Tech Startups

Davide Di Dio

Many interesting applications emerged with the increasing popularity of deep learning. This project explored natural language processing and visualization techniques as well as two neural network architectures to classify ICOs. The first network focused on the ICOs white-paper with a bi-directional LSTM attention network. The second targeted the ICOs website structure with a Graph neural network as well as page topics with Latent Dirichlet Analysis.

2019

A Probabilistic Approach to Socio-Geographic Reality Mining

Katayoun Farrahi

EPFL2011

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022