New GPU algorithm speeds up Gaussian Process analysis.
problem Reducing computational complexity for large spatial datasets.
method Implemented three GPU methods for Vecchia Approximation.
result New GPU method outperforms existing methods.
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Paper presents an efficient approach for integrating LSTM language models in LVCSR systems.
problem Difficulty in incorporating LSTM language models efficiently into LVCSR systems.
method First-pass decoding with LSTM-LM followed by lattice rescoring and hypothesis recombination.
result Competitive results on Hub5'00 and Librispeech corpora with runtime better than real-time.
SIMD operations boost Bayesian computations up to 6x faster.
problem Expensive Bayesian computations are computationally intensive and parallelizable.
method Demonstrated the utility of SIMD operations for Bayesian applications using standard libraries.
result Up to 6x improvement in floating point arithmetic performance.
The automatic digitizing of paper maps is a significant and challenging task for both academia and industry. As an important procedure of map digitizing, the semantic segmentation section mainly relies on manual visual interpretation with low efficiency. In this study, we select urban planning maps as a representative …
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the inference phase of DNNs. However, it can be hardly used in the training phase because the training phase involves dense matrix-multiplicatio…
The pricing of American style and multiple exercise options is a very challenging problem in mathematical finance. One usually employs a Least-Square Monte Carlo approach (Longstaff-Schwartz method) for the evaluation of conditional expectations which arise in the Backward Dynamic Programming principle for such optimal…
The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data. tSNE reveals clusters of high-dimensional data points at different scales while it requires only minimal tuning of its pa…
The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in im…