4,669 research outputs found
Enhancing Domain Word Embedding via Latent Semantic Imputation
We present a novel method named Latent Semantic Imputation (LSI) to transfer
external knowledge into semantic space for enhancing word embedding. The method
integrates graph theory to extract the latent manifold structure of the
entities in the affinity space and leverages non-negative least squares with
standard simplex constraints and power iteration method to derive spectral
embeddings. It provides an effective and efficient approach to combining entity
representations defined in different Euclidean spaces. Specifically, our
approach generates and imputes reliable embedding vectors for low-frequency
words in the semantic space and benefits downstream language tasks that depend
on word embedding. We conduct comprehensive experiments on a carefully designed
classification problem and language modeling and demonstrate the superiority of
the enhanced embedding via LSI over several well-known benchmark embeddings. We
also confirm the consistency of the results under different parameter settings
of our method.Comment: ACM SIGKDD 201
Evolutionary Approaches to Minimizing Network Coding Resources
We wish to minimize the resources used for network coding while achieving the
desired throughput in a multicast scenario. We employ evolutionary approaches,
based on a genetic algorithm, that avoid the computational complexity that
makes the problem NP-hard. Our experiments show great improvements over the
sub-optimal solutions of prior methods. Our new algorithms improve over our
previously proposed algorithm in three ways. First, whereas the previous
algorithm can be applied only to acyclic networks, our new method works also
with networks with cycles. Second, we enrich the set of components used in the
genetic algorithm, which improves the performance. Third, we develop a novel
distributed framework. Combining distributed random network coding with our
distributed optimization yields a network coding protocol where the resources
used for coding are optimized in the setup phase by running our evolutionary
algorithm at each node of the network. We demonstrate the effectiveness of our
approach by carrying out simulations on a number of different sets of network
topologies.Comment: 9 pages, 6 figures, accepted to the 26th Annual IEEE Conference on
Computer Communications (INFOCOM 2007
Symmetric Strategy Improvement
Symmetry is inherent in the definition of most of the two-player zero-sum
games, including parity, mean-payoff, and discounted-payoff games. It is
therefore quite surprising that no symmetric analysis techniques for these
games exist. We develop a novel symmetric strategy improvement algorithm where,
in each iteration, the strategies of both players are improved simultaneously.
We show that symmetric strategy improvement defies Friedmann's traps, which
shook the belief in the potential of classic strategy improvement to be
polynomial
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