29,667 research outputs found
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Deep networks consume a large amount of memory by their nature. A natural
question arises can we reduce that memory requirement whilst maintaining
performance. In particular, in this work we address the problem of memory
efficient learning for multiple tasks. To this end, we propose a novel network
architecture producing multiple networks of different configurations, termed
deep virtual networks (DVNs), for different tasks. Each DVN is specialized for
a single task and structured hierarchically. The hierarchical structure, which
contains multiple levels of hierarchy corresponding to different numbers of
parameters, enables multiple inference for different memory budgets. The
building block of a deep virtual network is based on a disjoint collection of
parameters of a network, which we call a unit. The lowest level of hierarchy in
a deep virtual network is a unit, and higher levels of hierarchy contain lower
levels' units and other additional units. Given a budget on the number of
parameters, a different level of a deep virtual network can be chosen to
perform the task. A unit can be shared by different DVNs, allowing multiple
DVNs in a single network. In addition, shared units provide assistance to the
target task with additional knowledge learned from another tasks. This
cooperative configuration of DVNs makes it possible to handle different tasks
in a memory-aware manner. Our experiments show that the proposed method
outperforms existing approaches for multiple tasks. Notably, ours is more
efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201
Rumble: Data Independence for Large Messy Data Sets
This paper introduces Rumble, an engine that executes JSONiq queries on
large, heterogeneous and nested collections of JSON objects, leveraging the
parallel capabilities of Spark so as to provide a high degree of data
independence. The design is based on two key insights: (i) how to map JSONiq
expressions to Spark transformations on RDDs and (ii) how to map JSONiq FLWOR
clauses to Spark SQL on DataFrames. We have developed a working implementation
of these mappings showing that JSONiq can efficiently run on Spark to query
billions of objects into, at least, the TB range. The JSONiq code is concise in
comparison to Spark's host languages while seamlessly supporting the nested,
heterogeneous data sets that Spark SQL does not. The ability to process this
kind of input, commonly found, is paramount for data cleaning and curation. The
experimental analysis indicates that there is no excessive performance loss,
occasionally even a gain, over Spark SQL for structured data, and a performance
gain over PySpark. This demonstrates that a language such as JSONiq is a simple
and viable approach to large-scale querying of denormalized, heterogeneous,
arborescent data sets, in the same way as SQL can be leveraged for structured
data sets. The results also illustrate that Codd's concept of data independence
makes as much sense for heterogeneous, nested data sets as it does on highly
structured tables.Comment: Preprint, 9 page
Deep Metric Learning via Lifted Structured Feature Embedding
Learning the distance metric between pairs of examples is of great importance
for learning and visual recognition. With the remarkable success from the state
of the art convolutional neural networks, recent works have shown promising
results on discriminatively training the networks to learn semantic feature
embeddings where similar examples are mapped close to each other and dissimilar
examples are mapped farther apart. In this paper, we describe an algorithm for
taking full advantage of the training batches in the neural network training by
lifting the vector of pairwise distances within the batch to the matrix of
pairwise distances. This step enables the algorithm to learn the state of the
art feature embedding by optimizing a novel structured prediction objective on
the lifted problem. Additionally, we collected Online Products dataset: 120k
images of 23k classes of online products for metric learning. Our experiments
on the CUB-200-2011, CARS196, and Online Products datasets demonstrate
significant improvement over existing deep feature embedding methods on all
experimented embedding sizes with the GoogLeNet network.Comment: 11 page
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