The Variational Homoencoder: Learning to learn high capacity generative models from few examples

Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. This technique, which we call a Variational Homoencoder (VHE), produces a hierarchical latent variable model which better utilises latent variables. We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks. We additionally validate the VHE on natural images from the YouTube Faces database. Finally, we develop extensions of the model that apply to richer dataset structures such as factorial and hierarchical categories.Comment: UAI 2018 oral presentatio

The Variational Homoencoder: Learning to learn high capacity generative models from few examples

© 34th Conference on Uncertainty in Artificial Intelligence 2018. All rights reserved. Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. This technique, which we call a Variational Homoencoder (VHE), produces a hierarchical latent variable model which better utilises latent variables. We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks. We additionally validate the VHE on natural images from the YouTube Faces database. Finally, we develop extensions of the model that apply to richer dataset structures such as factorial and hierarchical categories

Targeted inactivation of fh1 causes proliferative renal cyst development and activation of the hypoxia pathway.

Germline mutations in the fumarate hydratase (FH) tumor suppressor gene predispose to leiomyomatosis, renal cysts, and renal cell cancer (HLRCC). HLRCC tumors overexpress HIF1alpha and hypoxia pathway genes. We conditionally inactivated mouse Fh1 in the kidney. Fh1 mutants developed multiple clonal renal cysts that overexpressed Hif1alpha and Hif2alpha. Hif targets, such as Glut1 and Vegf, were upregulated. We found that Fh1-deficient murine embryonic stem cells and renal carcinomas from HLRCC showed similar overexpression of HIF and hypoxia pathway components to the mouse cysts. Our data have shown in vivo that pseudohypoxic drive, resulting from HIF1alpha (and HIF2alpha) overexpression, is a direct consequence of Fh1 inactivation. Our mouse may be useful for testing therapeutic interventions that target angiogenesis and HIF-prolyl hydroxylation

Expression profiling in progressive stages of fumarate-hydratase deficiency: the contribution of metabolic changes to tumorigenesis.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is caused by mutations in the Krebs cycle enzyme fumarate hydratase (FH). It has been proposed that "pseudohypoxic" stabilization of hypoxia-inducible factor-α (HIF-α) by fumarate accumulation contributes to tumorigenesis in HLRCC. We hypothesized that an additional direct consequence of FH deficiency is the establishment of a biosynthetic milieu. To investigate this hypothesis, we isolated primary mouse embryonic fibroblast (MEF) lines from Fh1-deficient mice. As predicted, these MEFs upregulated Hif-1α and HIF target genes directly as a result of FH deficiency. In addition, detailed metabolic assessment of these MEFs confirmed their dependence on glycolysis, and an elevated rate of lactate efflux, associated with the upregulation of glycolytic enzymes known to be associated with tumorigenesis. Correspondingly, Fh1-deficient benign murine renal cysts and an advanced human HLRCC-related renal cell carcinoma manifested a prominent and progressive increase in the expression of HIF-α target genes and in genes known to be relevant to tumorigenesis and metastasis. In accord with our hypothesis, in a variety of different FH-deficient tissues, including a novel murine model of Fh1-deficient smooth muscle, we show a striking and progressive upregulation of a tumorigenic metabolic profile, as manifested by increased PKM2 and LDHA protein. Based on the models assessed herein, we infer that that FH deficiency compels cells to adopt an early, reversible, and progressive protumorigenic metabolic milieu that is reminiscent of that driving the Warburg effect. Targets identified in these novel and diverse FH-deficient models represent excellent potential candidates for further mechanistic investigation and therapeutic metabolic manipulation in tumors