5,581,427 research outputs found
Towards an analysis of shear suspension flows using radial basis functions
In this paper, radial basis functions are utilised for numerical prediction of the bulk properties of
particulate suspensions under simple shear conditions. The
suspending fluid is Newtonian and the suspended particles are rigid. Results obtained are compared well with those based on finite elements in the literature
Sequence to Sequence -- Video to Text
Real-world videos often have complex dynamics; and methods for generating
open-domain video descriptions should be sensitive to temporal structure and
allow both input (sequence of frames) and output (sequence of words) of
variable length. To approach this problem, we propose a novel end-to-end
sequence-to-sequence model to generate captions for videos. For this we exploit
recurrent neural networks, specifically LSTMs, which have demonstrated
state-of-the-art performance in image caption generation. Our LSTM model is
trained on video-sentence pairs and learns to associate a sequence of video
frames to a sequence of words in order to generate a description of the event
in the video clip. Our model naturally is able to learn the temporal structure
of the sequence of frames as well as the sequence model of the generated
sentences, i.e. a language model. We evaluate several variants of our model
that exploit different visual features on a standard set of YouTube videos and
two movie description datasets (M-VAD and MPII-MD).Comment: ICCV 2015 camera-ready. Includes code, project page and LSMDC
challenge result
Sequence to Sequence Learning with Neural Networks
Deep Neural Networks (DNNs) are powerful models that have achieved excellent
performance on difficult learning tasks. Although DNNs work well whenever large
labeled training sets are available, they cannot be used to map sequences to
sequences. In this paper, we present a general end-to-end approach to sequence
learning that makes minimal assumptions on the sequence structure. Our method
uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to
a vector of a fixed dimensionality, and then another deep LSTM to decode the
target sequence from the vector. Our main result is that on an English to
French translation task from the WMT'14 dataset, the translations produced by
the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's
BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did
not have difficulty on long sentences. For comparison, a phrase-based SMT
system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM
to rerank the 1000 hypotheses produced by the aforementioned SMT system, its
BLEU score increases to 36.5, which is close to the previous best result on
this task. The LSTM also learned sensible phrase and sentence representations
that are sensitive to word order and are relatively invariant to the active and
the passive voice. Finally, we found that reversing the order of the words in
all source sentences (but not target sentences) improved the LSTM's performance
markedly, because doing so introduced many short term dependencies between the
source and the target sentence which made the optimization problem easier.Comment: 9 page
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