2,979 research outputs found
Sequence to Sequence Mixture Model for Diverse Machine Translation
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated
translations. This can be attributed to the limitation of SEQ2SEQ models in
capturing lexical and syntactic variations in a parallel corpus resulting from
different styles, genres, topics, or ambiguity of the translation process. In
this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that
improves both translation diversity and quality by adopting a committee of
specialized translation models rather than a single translation model. Each
mixture component selects its own training dataset via optimization of the
marginal loglikelihood, which leads to a soft clustering of the parallel
corpus. Experiments on four language pairs demonstrate the superiority of our
mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted
beam search. Our mixture model uses negligible additional parameters and incurs
no extra computation cost during decoding.Comment: 11 pages, 5 figures, accepted to CoNLL201
Fast Exact Search in Hamming Space with Multi-Index Hashing
There is growing interest in representing image data and feature descriptors
using compact binary codes for fast near neighbor search. Although binary codes
are motivated by their use as direct indices (addresses) into a hash table,
codes longer than 32 bits are not being used as such, as it was thought to be
ineffective. We introduce a rigorous way to build multiple hash tables on
binary code substrings that enables exact k-nearest neighbor search in Hamming
space. The approach is storage efficient and straightforward to implement.
Theoretical analysis shows that the algorithm exhibits sub-linear run-time
behavior for uniformly distributed codes. Empirical results show dramatic
speedups over a linear scan baseline for datasets of up to one billion codes of
64, 128, or 256 bits
Analysing Risk Management in Banks: Evidence of Bank Efficiency and Macroeconomic Impact
The recent Global Economic meltdown triggered by the subprime mortgage crisis of United States in 2007 and its adverse effect on financial markets and participants in the financial industry worldwide have resulted in a capital management crisis in most financial institutions especially banks. This study is a case for the Nigerian banking industry, focusing on factors affecting risk management efficiency in banks. For empirical investigation, we employed Panel regression analysis taking a stratum of time series data and cross-sectional variants of macro and bank-specific factors for period covering 2003 to 2009. Result for panel regression indicates that risk management efficiency in Nigerian banks is not just affected by bank-specific factors but also by macroeconomic variables. This describes the pro-cyclicality of bank performance in the Nigerian banking sector. As it stands, the sufficiency of Basel principles for risk management is doubtful because asset quality varies with business cycles.Risk management; Nigerian banks; capital adequacy; Basel; cyclicality
Dynamic Facility Location via Exponential Clocks
The \emph{dynamic facility location problem} is a generalization of the
classic facility location problem proposed by Eisenstat, Mathieu, and Schabanel
to model the dynamics of evolving social/infrastructure networks. The
generalization lies in that the distance metric between clients and facilities
changes over time. This leads to a trade-off between optimizing the classic
objective function and the "stability" of the solution: there is a switching
cost charged every time a client changes the facility to which it is connected.
While the standard linear program (LP) relaxation for the classic problem
naturally extends to this problem, traditional LP-rounding techniques do not,
as they are often sensitive to small changes in the metric resulting in
frequent switches.
We present a new LP-rounding algorithm for facility location problems, which
yields the first constant approximation algorithm for the dynamic facility
location problem. Our algorithm installs competing exponential clocks on the
clients and facilities, and connect every client by the path that repeatedly
follows the smallest clock in the neighborhood. The use of exponential clocks
gives rise to several properties that distinguish our approach from previous
LP-roundings for facility location problems. In particular, we use \emph{no
clustering} and we allow clients to connect through paths of \emph{arbitrary
lengths}. In fact, the clustering-free nature of our algorithm is crucial for
applying our LP-rounding approach to the dynamic problem
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