8,891 research outputs found
Separability, Locality, and Higher Dimensions in Quantum Mechanics
*A shortened version of this paper will appear in Current Controversies in Philosophy of Science, Dasgupta and Weslake, eds. Routledge.*
This paper describes the case that can be made for a high-dimensional ontology in quantum mechanics based on the virtues of avoiding both nonseparability and non locality
Photometric observations of recent comets
Infrared observations of comet Bennett, Kohoutek, Bradfield, and Encke are analyzed with emphasis on the detection of the silicate emission feature. Results are summarized
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
We compare the fast training and decoding speed of RETURNN of attention
models for translation, due to fast CUDA LSTM kernels, and a fast pure
TensorFlow beam search decoder. We show that a layer-wise pretraining scheme
for recurrent attention models gives over 1% BLEU improvement absolute and it
allows to train deeper recurrent encoder networks. Promising preliminary
results on max. expected BLEU training are presented. We are able to train
state-of-the-art models for translation and end-to-end models for speech
recognition and show results on WMT 2017 and Switchboard. The flexibility of
RETURNN allows a fast research feedback loop to experiment with alternative
architectures, and its generality allows to use it on a wide range of
applications.Comment: accepted as demo paper on ACL 201
Robust Beam Search for Encoder-Decoder Attention Based Speech Recognition without Length Bias
As one popular modeling approach for end-to-end speech recognition,
attention-based encoder-decoder models are known to suffer the length bias and
corresponding beam problem. Different approaches have been applied in simple
beam search to ease the problem, most of which are heuristic-based and require
considerable tuning. We show that heuristics are not proper modeling
refinement, which results in severe performance degradation with largely
increased beam sizes. We propose a novel beam search derived from
reinterpreting the sequence posterior with an explicit length modeling. By
applying the reinterpreted probability together with beam pruning, the obtained
final probability leads to a robust model modification, which allows reliable
comparison among output sequences of different lengths. Experimental
verification on the LibriSpeech corpus shows that the proposed approach solves
the length bias problem without heuristics or additional tuning effort. It
provides robust decision making and consistently good performance under both
small and very large beam sizes. Compared with the best results of the
heuristic baseline, the proposed approach achieves the same WER on the 'clean'
sets and 4% relative improvement on the 'other' sets. We also show that it is
more efficient with the additional derived early stopping criterion.Comment: accepted at INTERSPEECH202
Beyond neighborhood revitalization
After community groups rescue a troubled neighborhood, the area may attract many newcomers—and become expensive. One community shows how to help activists retain the diversity of the towns they work so hard to revitalize.Community development - Massachusetts ; Housing - Prices ; Housing - Boston
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