Contributions to generative models and their applications

Generative models are a large class of machine learning models for unsupervised learning. They have various applications in machine learning and artificial intelligence. In this thesis, we discuss many aspects of generative models and their applications to other machine learning problems. In particular, we discuss several important topics in generative models, including how to stabilize discrete GAN training with importance sampling, how to do better sampling from GANs using a connection with energy-based models, how to better train auto-regressive models with the help of an energy-based model formulation, as well as two applications of generative models to other machine learning problems, one about residual networks, the other about safety verification.Les modèles génératifs sont une grande classe de modèles d’apprentissage automatique pour l’apprentissage non supervisé. Ils ont diverses applications dans l’apprentissage automatique et l’intelligence artificielle. Dans cette thèse, nous discutons de nombreux aspects des modèles génératifs et de leurs applications à d’autres problèmes d’apprentissage automatique. En particulier, nous discutons de plusieurs sujets importants dans les modèles génératifs, y compris comment stabiliser la formation GAN discrète avec un échantillonnage d’importance, comment faire un meilleur échantillonnage à partir de GAN en utilisant une connexion avec des modèles basés sur l’énergie, comment mieux former des modèles auto-régressifs avec l’aide d’une formulation de modèle basée sur l’énergie, ainsi que deux applications de modèles génératifs à d’autres problèmes d’apprentissage automatique, l’une sur les réseaux résiduels, l’autre sur la vérification de la sécurité

Mode Regularized Generative Adversarial Networks

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.Comment: Published as a conference paper at ICLR 201

Theoretical studies on the photophysical properties of luminescent pincer gold(III) arylacetylide complexes: the role of π-conjugation at the C-deprotonated [C^N^C] ligand

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Investigating the Non-Linear Relationships in the Expectancy Theory: The Case of Crowdsourcing Marketplace

Crowdsourcing marketplace as a new platform for companies or individuals to source ideas or works from the public has become popular in the contemporary world. A key issue about the sustainability of this type of marketplace relies on the effort that problem solvers expend on the online tasks. However, the predictors of effort investment in the crowdsourcing context is rarely investigated. In this study, based on the expectancy theory which suggests the roles of reward valence, trust and self efficacy, we develop a research model to study the factors influencing effort. Further, the non-linear relationships between self efficacy and effort is proposed. Based on a field survey, we found that: (1) reward valence and trust positively influence effort; (2) when task complexity is high, there will be a convex relationship between self efficacy and effort; and (3) when task complexity is low, there will be a concave relationship between self efficacy and effort. Theoretical and practical implications are also discussed

Verifying Concurrent Data Structures Using Data-Expansion

We present the first thread modular proof of a highly concurrent binary search tree. This proof tackles the problem of reasoning about complicated thread interferences using only thread modular invariants. The key tool in this proof is the Data-Expansion Lemma, a novel lemma that allows us to reason about search operations in any given state. We highlight the power of this lemma when combined with our generalized version of the classical Hindsight Lemma, which enables us to prove linearizability by reasoning about the temporal properties of the operations instead of reasoning about the linearizability points directly. The Data-Expansion Lemma provides an interesting solution to the proof blowup prob-lem when reasoning about concurrent data structures by separating the verification of effectful and effectless operations. We show that our proof methodology is widely applicable to several published algorithms and argue that many advanced highly concurrent data structures can be surprisingly easy to verify using thread-modular arguments