We propose a general framework for support vector machines (SVM) based on the principle of multi-objective optimization. The learning of SVMs is formulated as a multiobjective pro...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
The intersection of tree transducer-based translation models with n-gram language models results in huge dynamic programs for machine translation decoding. We propose a multipass,...
We present a new pointer analysis for use in shared memory programs running on hierarchical parallel machines. The analysis is motivated by the partitioned global address space lan...
We propose to search for common abstractions for different concurrency models to enable high-level language virtual machines to support a wide range of different concurrency model...