title stringlengths 18 99 | paper_url stringlengths 31 31 | authors listlengths 0 8 | type stringclasses 0
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values | abstract large_stringlengths 611 1.86k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
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|---|---|---|---|---|---|---|---|---|---|---|---|
Multilingual Distributed Representations without Word Alignment | https://arxiv.org/abs/1312.6173 | [
"Karl Moritz Hermann",
"Phil Blunsom"
] | null | null | Distributed representations of meaning are a natural way to encode covariance
relationships between words and phrases in NLP. By overcoming data sparsity
problems, as well as providing information about semantic relatedness which is
not available in discrete representations, distributed representations have
proven us... | [] | null | 1 | 1312.6173 | iclr_archive | [
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Zero-Shot Learning by Convex Combination of Semantic Embeddings | https://arxiv.org/abs/1312.5650 | [
"Mohammad Norouzi",
"Tomas Mikolov",
"Samy Bengio",
"Yoram Singer",
"Jonathon Shlens",
"Andrea Frome",
"Greg S. Corrado",
"Jeffrey Dean"
] | null | null | Several recent publications have proposed methods for mapping images into
continuous semantic embedding spaces. In some cases the embedding space is
trained jointly with the image transformation. In other cases the semantic
embedding space is established by an independent natural language processing
task, and then th... | [] | null | 2 | 1312.5650 | iclr_archive | [
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks | https://arxiv.org/abs/1312.6120 | [
"Andrew M. Saxe",
"James L. McClelland",
"Surya Ganguli"
] | null | null | Despite the widespread practical success of deep learning methods, our
theoretical understanding of the dynamics of learning in deep neural networks
remains quite sparse. We attempt to bridge the gap between the theory and
practice of deep learning by systematically analyzing learning dynamics for the
restricted case... | [] | null | 3 | 1312.6120 | iclr_archive | [
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Revisiting Natural Gradient for Deep Networks | https://arxiv.org/abs/1301.3584 | [
"Razvan Pascanu",
"Yoshua Bengio"
] | null | null | We evaluate natural gradient, an algorithm originally proposed in Amari
(1997), for learning deep models. The contributions of this paper are as
follows. We show the connection between natural gradient and three other
recently proposed methods for training deep models: Hessian-Free (Martens,
2010), Krylov Subspace De... | [] | null | 4 | 1301.3584 | iclr_archive | [
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Unit Tests for Stochastic Optimization | https://arxiv.org/abs/1312.6055 | [] | null | null | Optimization by stochastic gradient descent is an important component of many
large-scale machine learning algorithms. A wide variety of such optimization
algorithms have been devised; however, it is unclear whether these algorithms
are robust and widely applicable across many different optimization landscapes.
In th... | [] | null | 5 | 1312.6055 | iclr_archive | [
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... |
The return of AdaBoost.MH: multi-class Hamming trees | https://arxiv.org/abs/1312.6086 | [
"Balázs Kégl"
] | null | null | Within the framework of AdaBoost.MH, we propose to train vector-valued
decision trees to optimize the multi-class edge without reducing the
multi-class problem to $K$ binary one-against-all classifications. The key
element of the method is a vector-valued decision stump, factorized into an
input-independent vector of... | [] | null | 6 | 1312.6086 | iclr_archive | [
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... |
Neuronal Synchrony in Complex-Valued Deep Networks | https://arxiv.org/abs/1312.6115 | [
"David P. Reichert",
"Thomas Serre"
] | null | null | Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identif... | [] | null | 7 | 1312.6115 | iclr_archive | [
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Bounding the Test Log-Likelihood of Generative Models | https://arxiv.org/abs/1311.6184 | [
"Yoshua Bengio",
"Li Yao",
"KyungHyun Cho"
] | null | null | Several interesting generative learning algorithms involve a complex
probability distribution over many random variables, involving intractable
normalization constants or latent variable normalization. Some of them may even
not have an analytic expression for the unnormalized probability function and
no tractable app... | [] | null | 8 | 1311.6184 | iclr_archive | [
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A Generative Product-of-Filters Model of Audio | https://arxiv.org/abs/1312.5857 | [
"Dawen Liang",
"Mathew D. Hoffman",
"Gautham Mysore"
] | null | null | We propose the product-of-filters (PoF) model, a generative model that
decomposes audio spectra as sparse linear combinations of "filters" in the
log-spectral domain. PoF makes similar assumptions to those used in the classic
homomorphic filtering approach to signal processing, but replaces hand-designed
decompositio... | [] | null | 9 | 1312.5857 | iclr_archive | [
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How to Construct Deep Recurrent Neural Networks | https://arxiv.org/abs/1312.6026 | [
"Razvan Pascanu",
"Caglar Gulcehre",
"Kyunghyun Cho",
"Yoshua Bengio"
] | null | null | In this paper, we explore different ways to extend a recurrent neural network
(RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in
an RNN is not as clear as it is in feedforward neural networks. By carefully
analyzing and understanding the architecture of an RNN, however, we find three
point... | [] | null | 10 | 1312.6026 | iclr_archive | [
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Zero-Shot Learning and Clustering for Semantic Utterance Classification | https://arxiv.org/abs/1401.0509 | [
"Yann N. Dauphin",
"Gokhan Tur",
"Dilek Hakkani-Tur",
"Larry Heck"
] | null | null | We propose a novel zero-shot learning method for semantic utterance
classification (SUC). It learns a classifier $f: X \to Y$ for problems where
none of the semantic categories $Y$ are present in the training set. The
framework uncovers the link between categories and utterances using a semantic
space. We show that t... | [] | null | 11 | 1401.0509 | iclr_archive | [
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0.00147... |
An empirical analysis of dropout in piecewise linear networks | https://arxiv.org/abs/1312.6197 | [
"David Warde-Farley",
"Ian J. Goodfellow",
"Aaron Courville",
"Yoshua Bengio"
] | null | null | The recently introduced dropout training criterion for neural networks has
been the subject of much attention due to its simplicity and remarkable
effectiveness as a regularizer, as well as its interpretation as a training
procedure for an exponentially large ensemble of networks that share
parameters. In this work w... | [] | null | 12 | 1312.6197 | iclr_archive | [
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... |
An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks | https://arxiv.org/abs/1312.6211 | [
"Ian J. Goodfellow",
"Mehdi Mirza",
"Da Xiao",
"Aaron Courville",
"Yoshua Bengio"
] | null | null | Catastrophic forgetting is a problem faced by many machine learning models
and algorithms. When trained on one task, then trained on a second task, many
machine learning models "forget" how to perform the first task. This is widely
believed to be a serious problem for neural networks. Here, we investigate the
extent ... | [] | null | 13 | 1312.6211 | iclr_archive | [
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0.022898854687809944,
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... |
On Fast Dropout and its Applicability to Recurrent Networks | https://arxiv.org/abs/1311.0701 | [
"Justin Bayer",
"Christian Osendorfer",
"Daniela Korhammer",
"Nutan Chen",
"Sebastian Urban",
"Patrick van der Smagt"
] | null | null | Recurrent Neural Networks (RNNs) are rich models for the processing of
sequential data. Recent work on advancing the state of the art has been focused
on the optimization or modelling of RNNs, mostly motivated by adressing the
problems of the vanishing and exploding gradients. The control of overfitting
has seen cons... | [] | null | 14 | 1311.0701 | iclr_archive | [
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Network In Network | https://arxiv.org/abs/1312.4400 | [
"Min Lin",
"Qiang Chen",
"Shuicheng Yan"
] | null | null | We propose a novel deep network structure called "Network In Network" (NIN)
to enhance model discriminability for local patches within the receptive field.
The conventional convolutional layer uses linear filters followed by a
nonlinear activation function to scan the input. Instead, we build micro neural
networks wi... | [] | null | 15 | 1312.4400 | iclr_archive | [
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Auto-Encoding Variational Bayes | https://arxiv.org/abs/1312.6114 | [
"Diederik P. Kingma",
"Max Welling"
] | null | null | How can we perform efficient inference and learning in directed probabilistic
models, in the presence of continuous latent variables with intractable
posterior distributions, and large datasets? We introduce a stochastic
variational inference and learning algorithm that scales to large datasets and,
under some mild d... | [] | null | 16 | 1312.6114 | iclr_archive | [
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Group-sparse Embeddings in Collective Matrix Factorization | https://arxiv.org/abs/1312.5921 | [
"Arto Klami",
"Guillaume Bouchard",
"Abhishek Tripathi"
] | null | null | CMF is a technique for simultaneously learning low-rank representations based
on a collection of matrices with shared entities. A typical example is the
joint modeling of user-item, item-property, and user-feature matrices in a
recommender system. The key idea in CMF is that the embeddings are shared
across the matri... | [] | null | 17 | 1312.5921 | iclr_archive | [
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... |
Learning Human Pose Estimation Features with Convolutional Networks | https://arxiv.org/abs/1312.7302 | [
"Ajrun Jain",
"Jonathan Tompson",
"Mykhaylo Andriluka",
"Graham W. Taylor",
"Christoph Bregler"
] | null | null | This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and o... | [] | null | 18 | 1312.7302 | iclr_archive | [
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EXMOVES: Classifier-based Features for Scalable Action Recognition | https://arxiv.org/abs/1312.5785 | [
"Du Tran",
"Lorenzo Torresani"
] | null | null | This paper introduces EXMOVES, learned exemplar-based features for efficient
recognition of actions in videos. The entries in our descriptor are produced by
evaluating a set of movement classifiers over spatial-temporal volumes of the
input sequence. Each movement classifier is a simple exemplar-SVM trained on
low-le... | [] | null | 19 | 1312.5785 | iclr_archive | [
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0.0... |
On the number of inference regions of deep feed forward networks with piece-wise linear activations | https://arxiv.org/abs/1312.6098 | [
"Razvan Pascanu",
"Guido Montufar",
"Yoshua Bengio"
] | null | null | This paper explores the complexity of deep feedforward networks with linear
pre-synaptic couplings and rectified linear activations. This is a contribution
to the growing body of work contrasting the representational power of deep and
shallow network architectures. In particular, we offer a framework for
comparing de... | [] | null | 20 | 1312.6098 | iclr_archive | [
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Intriguing properties of neural networks | https://arxiv.org/abs/1312.6199 | [
"Christian Szegedy",
"Wojciech Zaremba",
"Ilya Sutskever",
"Joan Bruna",
"Dumitru Erhan",
"Ian Goodfellow",
"Rob Fergus"
] | null | null | Deep neural networks are highly expressive models that have recently achieved
state of the art performance on speech and visual recognition tasks. While
their expressiveness is the reason they succeed, it also causes them to learn
uninterpretable solutions that could have counter-intuitive properties. In this
paper w... | [] | null | 21 | 1312.6199 | iclr_archive | [
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0.... |
Fast Training of Convolutional Networks through FFTs | https://arxiv.org/abs/1312.5851 | [
"Michael Mathieu",
"Mikael Henaff",
"Yann LeCun"
] | null | null | Convolutional networks are one of the most widely employed architectures in
computer vision and machine learning. In order to leverage their ability to
learn complex functions, large amounts of data are required for training.
Training a large convolutional network to produce state-of-the-art results can
take weeks, e... | [] | null | 22 | 1312.5851 | iclr_archive | [
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0... |
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling | https://arxiv.org/abs/1310.0354 | [
"Gary B. Huang",
"Viren Jain"
] | null | null | Feedforward multilayer networks trained by supervised learning have recently
demonstrated state of the art performance on image labeling problems such as
boundary prediction and scene parsing. As even very low error rates can limit
practical usage of such systems, methods that perform closer to human accuracy
remain ... | [] | null | 23 | 1310.0354 | iclr_archive | [
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Some Improvements on Deep Convolutional Neural Network Based Image Classification | https://arxiv.org/abs/1312.5402 | [
"Andrew G. Howard"
] | null | null | We investigate multiple techniques to improve upon the current state of the
art deep convolutional neural network based image classification pipeline. The
techiques include adding more image transformations to training data, adding
more transformations to generate additional predictions at test time and using
complem... | [] | null | 24 | 1312.5402 | iclr_archive | [
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Deep Convolutional Ranking for Multilabel Image Annotation | https://arxiv.org/abs/1312.4894 | [
"Yunchao Gong",
"Yangqing Jia",
"Thomas Leung",
"Alexander Toshev",
"Sergey Ioffe"
] | null | null | Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work... | [] | null | 25 | 1312.4894 | iclr_archive | [
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-0.0439162477850914,
-0.02987098880112171,
-0.015329896472394466,
-0.08018685132265091,
0.... |
Learning to encode motion using spatio-temporal synchrony | https://arxiv.org/abs/1306.3162 | [
"Kishore Reddy Konda",
"Roland Memisevic",
"Vincent Michalski"
] | null | null | We consider the task of learning to extract motion from videos. To this end,
we show that the detection of spatial transformations can be viewed as the
detection of synchrony between the image sequence and a sequence of features
undergoing the motion we wish to detect. We show that learning about synchrony
is possibl... | [] | null | 26 | 1306.3162 | iclr_archive | [
0.03213730454444885,
0.006133718881756067,
0.017155049368739128,
0.04931074380874634,
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0.008499491959810257,
-0.03537948802113533,
-0.06463591754436493,
-0.019... |
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data | https://arxiv.org/abs/1312.6095 | [
"Bojan Pepik",
"Michael Stark",
"Peter Gehler",
"Bernt Schiele"
] | null | null | While the majority of today's object class models provide only 2D bounding
boxes, far richer output hypotheses are desirable including viewpoint,
fine-grained category, and 3D geometry estimate. However, models trained to
provide richer output require larger amounts of training data, preferably well
covering the rele... | [] | null | 27 | 1312.6095 | iclr_archive | [
0.0037162485532462597,
0.0195467509329319,
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-0.02684897370636463,
0.01890522800385952,
-0.06502818316221237,
-0.00... |
k-Sparse Autoencoders | https://arxiv.org/abs/1312.5663 | [
"Alireza Makhzani",
"Brendan Frey"
] | null | null | Recently, it has been observed that when representations are learnt in a way
that encourages sparsity, improved performance is obtained on classification
tasks. These methods involve combinations of activation functions, sampling
steps and different kinds of penalties. To investigate the effectiveness of
sparsity by ... | [] | null | 28 | 1312.5663 | iclr_archive | [
0.012596195563673973,
-0.03742688149213791,
0.0035575402434915304,
0.0422346331179142,
0.03648745268583298,
0.050974875688552856,
0.034396763890981674,
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-0.03175370395183563,
0.0018346074502915144,
-0.010639956220984459,
-0.049215637147426605,
0.02... |
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | https://arxiv.org/abs/1312.6229 | [
"Pierre Sermanet",
"Rob Fergus",
"Yann LeCun",
"Xiang Zhang",
"David Eigen",
"Michael Mathieu"
] | null | null | We present an integrated framework for using Convolutional Networks for
classification, localization and detection. We show how a multiscale and
sliding window approach can be efficiently implemented within a ConvNet. We
also introduce a novel deep learning approach to localization by learning to
predict object bound... | [] | null | 29 | 1312.6229 | iclr_archive | [
0.022380713373422623,
-0.03727075830101967,
0.014128649607300758,
0.027774879708886147,
0.032474126666784286,
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-0.04402013123035431,
-0.013300098478794098,
-0.010191853158175945,
-0.07991141080856323,
-0.... |
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks | https://arxiv.org/abs/1312.6082 | [
"Ian J. Goodfellow",
"Yaroslav Bulatov",
"Julian Ibarz",
"Sacha Arnoud",
"Vinay Shet"
] | null | null | Recognizing arbitrary multi-character text in unconstrained natural
photographs is a hard problem. In this paper, we address an equally hard
sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from
Street View imagery. Traditional approaches to solve this problem typically
separate out the local... | [] | null | 30 | 1312.6082 | iclr_archive | [
0.0030139607843011618,
-0.02436142973601818,
0.020039701834321022,
0.05803317949175835,
0.011937422677874565,
0.04306871071457863,
0.024566305801272392,
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-0.044662948697805405,
-0.020766090601682663,
0.013370760716497898,
-0.0886525884270668,
-0.0... |
Sequentially Generated Instance-Dependent Image Representations for Classification | https://arxiv.org/abs/1312.6594 | [
"Ludovic Denoyer",
"Matthieu Cord",
"Patrick Gallinari",
"Nicolas Thome",
"Gabriel Dulac-Arnold"
] | null | null | In this paper, we investigate a new framework for image classification that
adaptively generates spatial representations. Our strategy is based on a
sequential process that learns to explore the different regions of any image in
order to infer its category. In particular, the choice of regions is specific
to each ima... | [] | null | 31 | 1312.6594 | iclr_archive | [
0.019776536151766777,
0.008171012625098228,
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0.05620404705405235,
0.048756618052721024,
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-0.04530961811542511,
-0.00966894906014204,
-0.06250478327274323,
0.0... |
Learned versus Hand-Designed Feature Representations for 3d Agglomeration | https://arxiv.org/abs/1312.6159 | [
"John A. Bogovic",
"Gary B. Huang",
"Viren Jain"
] | null | null | For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
... | [] | null | 32 | 1312.6159 | iclr_archive | [
-0.0018228302942588925,
0.007658288814127445,
-0.005836880765855312,
0.004010969307273626,
0.03584088012576103,
0.03722967952489853,
0.00351541955024004,
-0.009942254051566124,
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-0.05831677466630936,
-0.02589370310306549,
-0.014049387536942959,
-0.06975946575403214,
0.... |
Spectral Networks and Locally Connected Networks on Graphs | https://arxiv.org/abs/1312.6203 | [
"Joan Bruna",
"Wojciech Zaremba",
"Arthur Szlam",
"Yann LeCun"
] | null | null | Convolutional Neural Networks are extremely efficient architectures in image
and audio recognition tasks, thanks to their ability to exploit the local
translational invariance of signal classes over their domain. In this paper we
consider possible generalizations of CNNs to signals defined on more general
domains wit... | [] | null | 33 | 1312.6203 | iclr_archive | [
-0.0006477616261690855,
-0.022800564765930176,
0.018682299181818962,
0.05119110271334648,
0.020177094265818596,
0.02447647415101528,
0.038164690136909485,
0.012600375339388847,
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-0.06660092622041702,
0.012816190719604492,
-0.0005596503033302724,
-0.07846560329198837,
... |
Sparse similarity-preserving hashing | https://arxiv.org/abs/1312.5479 | [
"Alex M. Bronstein",
"Pablo Sprechmann",
"Michael M. Bronstein",
"Jonathan Masci",
"Guillermo Sapiro"
] | null | null | In recent years, a lot of attention has been devoted to efficient nearest
neighbor search by means of similarity-preserving hashing. One of the plights
of existing hashing techniques is the intrinsic trade-off between performance
and computational complexity: while longer hash codes allow for lower false
positive rat... | [] | null | 34 | 1312.5479 | iclr_archive | [
-0.01743048056960106,
-0.03201700374484062,
-0.003935891669243574,
0.05760074779391289,
0.05057163164019585,
0.02539471909403801,
0.005494226701557636,
-0.006747093982994556,
-0.04221954941749573,
-0.04906972870230675,
-0.0007190327160060406,
-0.023024044930934906,
-0.059563010931015015,
0... |
Learning Transformations for Classification Forests | https://arxiv.org/abs/1312.5604 | [
"Qiang Qiu",
"Guillermo Sapiro"
] | null | null | This work introduces a transformation-based learner model for classification
forests. The weak learner at each split node plays a crucial role in a
classification tree. We propose to optimize the splitting objective by learning
a linear transformation on subspaces using nuclear norm as the optimization
criteria. The ... | [] | null | 35 | 1312.5604 | iclr_archive | [
-0.007970123551785946,
-0.03405546396970749,
-0.0012412575306370854,
0.011190669611096382,
0.046517811715602875,
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0.03588123619556427,
-0.04435154050588608,
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0.009624677710235119,
-0.018416455015540123,
-0.004043400753289461,
-0.06683732569217682,
0... |
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