MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast and pancreatic cancer. The source R code for this work is provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We introduce a method to generate vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function, we process images through a "probe network" and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is independent of details such as the number of classes and requires no understanding of the class label semantics. We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks. We demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a novel task. We present a simple meta-learning framework for learning a metric on embeddings that is capable of predicting which feature extractors will perform well on which task. Selecting a feature extractor with task embedding yields performance close to the best available feature extractor, with substantially less computational effort than exhaustively training and evaluating all available models.

This work addresses fine-grained image classification. Our work is based on the hypothesis that when dealing with subtle differences among object classes it is critical to identify and only account for a few informative image parts, as the remaining image context may not only be uninformative but may also hurt recognition. This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN). A state of this search is a set of proposal bounding boxes in the image, whose informativeness is evaluated by the heuristic function (H), and used for generating new candidate states by the successor function (S). The two functions are unified via a Long Short-Term Memory network (LSTM) into a new deep recurrent architecture, called HSnet. Thus, HSnet (i) generates proposals of informative image parts and (ii) fuses all proposals toward final fine-grained recognition. We specify both supervised and weakly supervised training of HSnet depending on the availability of object part annotations. Evaluation on the benchmark Caltech-UCSD Birds 200-2011 and Cars-196 datasets demonstrate our competitive performance relative to the state of the art.

This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupervised way. Such a summarizer can then be applied on a new video for estimating its optimal summarization. For learning, we specify a novel generative adversarial framework, consisting of the summarizer and discriminator. The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video. The discriminator is another LSTM aimed at distinguishing between the original video and its reconstruction from the summarizer. The summarizer LSTM is cast as an adversary of the discriminator, i.e., trained so as to maximally confuse the discriminator. This learning is also regularized for sparsity. Evaluation on four benchmark datasets, consisting of videos showing diverse events in first-and third-person views, demonstrates our competitive performance in comparison to fully supervised state-of-the-art approaches.

The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function. At prediction time, this approach must solve an often-challenging optimization problem. Search-based methods provide an alternative that has the potential to achieve higher performance. These methods learn to control a search procedure that constructs and evaluates candidate solutions. The recently-developed ℋC-Search method has been shown to achieve state-of-the-art results in natural language processing, but mixed success when applied to vision problems. This paper studies whether ℋC-Search can achieve similarly competitive performance on basic vision tasks such as object detection, scene labeling, and monocular depth estimation, where the leading paradigm is energy minimization. To this end, we introduce a search operator suited to the vision domain that improves a candidate solution by probabilistically sampling likely object configurations in the scene from the hierarchical Berkeley segmentation. We complement this search operator by applying the DAgger algorithm to robustly train the search heuristic so it learns from its previous mistakes. Our evaluation shows that these improvements reduce the branching factor and search depth, and thus give a significant performance boost. Our state-of-the-art results on scene labeling and depth estimation suggest that ℋC-Search provides a suitable tool for learning and inference in vision.