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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
When approaching a novel visual recognition problem in a specialized image
domain, a common strategy is to start with a pre-trained deep neural network
and fine-tune it to the specialized domain. If the target domain covers a
smaller visual space than the source domain used for pre-training (e.g.
ImageNet), the fine-tuned network is likely to be over-parameterized. However,
applying network pruning as a post-processing step to reduce the memory
requirements has drawbacks: fine-tuning and pruning are performed
independently; pruning parameters are set once and cannot adapt over time; and
the highly parameterized nature of state-of-the-art pruning methods make it
prohibitive to manually search the pruning parameter space for deep networks,
leading to coarse approximations. We propose a principled method for jointly
fine-tuning and compressing a pre-trained convolutional network that overcomes
these limitations. Experiments on two specialized image domains (remote sensing
images and describable textures) demonstrate the validity of the proposed
approach.Comment: BMVC 2017 ora
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
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