21,444 research outputs found
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs
DiffNodesets: An Efficient Structure for Fast Mining Frequent Itemsets
Mining frequent itemsets is an essential problem in data mining and plays an
important role in many data mining applications. In recent years, some itemset
representations based on node sets have been proposed, which have shown to be
very efficient for mining frequent itemsets. In this paper, we propose
DiffNodeset, a novel and more efficient itemset representation, for mining
frequent itemsets. Based on the DiffNodeset structure, we present an efficient
algorithm, named dFIN, to mining frequent itemsets. To achieve high efficiency,
dFIN finds frequent itemsets using a set-enumeration tree with a hybrid search
strategy and directly enumerates frequent itemsets without candidate generation
under some case. For evaluating the performance of dFIN, we have conduct
extensive experiments to compare it against with existing leading algorithms on
a variety of real and synthetic datasets. The experimental results show that
dFIN is significantly faster than these leading algorithms.Comment: 22 pages, 13 figure
BSP-fields: An Exact Representation of Polygonal Objects by Differentiable Scalar Fields Based on Binary Space Partitioning
The problem considered in this work is to find a dimension independent algorithm for the generation of signed scalar fields exactly representing polygonal objects and satisfying the following requirements: the defining real function takes zero value exactly at the polygonal object boundary; no extra zero-value isosurfaces should be generated; C1 continuity of the function in the entire domain. The proposed algorithms are based on the binary space partitioning (BSP) of the object by the planes passing through the polygonal faces and are independent of the object genus, the number of disjoint components, and holes in the initial polygonal mesh. Several extensions to the basic algorithm are proposed to satisfy the selected optimization criteria. The generated BSP-fields allow for applying techniques of the function-based modeling to already existing legacy objects from CAD and computer animation areas, which is illustrated by several examples
Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs
We develop data structures for dynamic closest pair problems with arbitrary
distance functions, that do not necessarily come from any geometric structure
on the objects. Based on a technique previously used by the author for
Euclidean closest pairs, we show how to insert and delete objects from an
n-object set, maintaining the closest pair, in O(n log^2 n) time per update and
O(n) space. With quadratic space, we can instead use a quadtree-like structure
to achieve an optimal time bound, O(n) per update. We apply these data
structures to hierarchical clustering, greedy matching, and TSP heuristics, and
discuss other potential applications in machine learning, Groebner bases, and
local improvement algorithms for partition and placement problems. Experiments
show our new methods to be faster in practice than previously used heuristics.Comment: 20 pages, 9 figures. A preliminary version of this paper appeared at
the 9th ACM-SIAM Symp. on Discrete Algorithms, San Francisco, 1998, pp.
619-628. For source code and experimental results, see
http://www.ics.uci.edu/~eppstein/projects/pairs
Threads and Or-Parallelism Unified
One of the main advantages of Logic Programming (LP) is that it provides an
excellent framework for the parallel execution of programs. In this work we
investigate novel techniques to efficiently exploit parallelism from real-world
applications in low cost multi-core architectures. To achieve these goals, we
revive and redesign the YapOr system to exploit or-parallelism based on a
multi-threaded implementation. Our new approach takes full advantage of the
state-of-the-art fast and optimized YAP Prolog engine and shares the underlying
execution environment, scheduler and most of the data structures used to
support YapOr's model. Initial experiments with our new approach consistently
achieve almost linear speedups for most of the applications, proving itself as
a good alternative for exploiting implicit parallelism in the currently
available low cost multi-core architectures.Comment: 17 pages, 21 figures, International Conference on Logic Programming
(ICLP 2010
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