title stringlengths 15 81 | paper_url stringlengths 31 31 | authors listlengths 1 6 | type stringclasses 2
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values | abstract large_stringlengths 616 1.37k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
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|---|---|---|---|---|---|---|---|---|---|---|---|
Word Representations via Gaussian Embedding | https://arxiv.org/abs/1412.6623 | [
"Luke Vilnis",
"Andrew McCallum"
] | Oral | null | Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product ... | [] | null | 1 | 1412.6623 | iclr_archive | [
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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | https://arxiv.org/abs/1412.6632 | [
"Junhua Mao",
"Wei Xu",
"Yi Yang",
"Jiang Wang",
"Alan Yuille"
] | Oral | null | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-netw... | [] | null | 2 | 1412.6632 | iclr_archive | [
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Deep Structured Output Learning for Unconstrained Text Recognition | https://arxiv.org/abs/1412.5903 | [
"Max Jaderberg",
"Karen Simonyan",
"Andrea Vedaldi",
"Andrew Zisserman"
] | Oral | null | We develop a representation suitable for the unconstrained recognition of
words in natural images: the general case of no fixed lexicon and unknown
length.
To this end we propose a convolutional neural network (CNN) based
architecture which incorporates a Conditional Random Field (CRF) graphical
model, taking the w... | [] | null | 3 | 1412.5903 | iclr_archive | [
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Very Deep Convolutional Networks for Large-Scale Image Recognition | https://arxiv.org/abs/1409.1556 | [
"Karen Simonyan",
"Andrew Zisserman"
] | Oral | null | In this work we investigate the effect of the convolutional network depth on
its accuracy in the large-scale image recognition setting. Our main
contribution is a thorough evaluation of networks of increasing depth using an
architecture with very small (3x3) convolution filters, which shows that a
significant improve... | [] | null | 4 | 1409.1556 | iclr_archive | [
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Fast Convolutional Nets With fbfft: A GPU Performance Evaluation | https://arxiv.org/abs/1412.7580 | [
"Nicolas Vasilache",
"Jeff Johnson",
"Michael Mathieu",
"Soumith Chintala",
"Serkan Piantino",
"Yann LeCun"
] | Oral | null | We examine the performance profile of Convolutional Neural Network training
on the current generation of NVIDIA Graphics Processing Units. We introduce two
new Fast Fourier Transform convolution implementations: one based on NVIDIA's
cuFFT library, and another based on a Facebook authored FFT implementation,
fbfft, t... | [] | null | 5 | 1412.7580 | iclr_archive | [
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Reweighted Wake-Sleep | https://arxiv.org/abs/1406.2751 | [
"Jorg Bornschein",
"Yoshua Bengio"
] | Oral | null | Training deep directed graphical models with many hidden variables and
performing inference remains a major challenge. Helmholtz machines and deep
belief networks are such models, and the wake-sleep algorithm has been proposed
to train them. The wake-sleep algorithm relies on training not just the
directed generative... | [] | null | 6 | 1406.2751 | iclr_archive | [
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The local low-dimensionality of natural images | https://arxiv.org/abs/1412.6626 | [
"Olivier Henaff",
"Johannes Balle",
"Neil Rabinowitz",
"Eero Simoncelli"
] | Oral | null | We develop a new statistical model for photographic images, in which the
local responses of a bank of linear filters are described as jointly Gaussian,
with zero mean and a covariance that varies slowly over spatial position. We
optimize sets of filters so as to minimize the nuclear norms of matrices of
their local a... | [] | null | 7 | 1412.6626 | iclr_archive | [
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Memory Networks | https://arxiv.org/abs/1410.3916 | [
"Jason Weston",
"Sumit Chopra",
"Antoine Bordes"
] | Oral | null | We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in... | [] | null | 8 | 1410.3916 | iclr_archive | [
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Object detectors emerge in Deep Scene CNNs | https://arxiv.org/abs/1412.6856 | [
"Bolei Zhou",
"Aditya Khosla",
"Agata Lapedriza",
"Aude Oliva",
"Antonio Torralba"
] | Oral | null | With the success of new computational architectures for visual processing,
such as convolutional neural networks (CNN) and access to image databases with
millions of labeled examples (e.g., ImageNet, Places), the state of the art in
computer vision is advancing rapidly. One important factor for continued
progress is ... | [] | null | 9 | 1412.6856 | iclr_archive | [
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Qualitatively characterizing neural network optimization problems | https://arxiv.org/abs/1412.6544 | [
"Ian Goodfellow",
"Oriol Vinyals"
] | Oral | null | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
a... | [] | null | 10 | 1412.6544 | iclr_archive | [
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Neural Machine Translation by Jointly Learning to Align and Translate | https://arxiv.org/abs/1409.0473 | [
"Dzmitry Bahdanau",
"Kyunghyun Cho",
"Yoshua Bengio"
] | Oral | null | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ... | [] | null | 11 | 1409.0473 | iclr_archive | [
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0.00... |
FitNets: Hints for Thin Deep Nets | https://arxiv.org/abs/1412.6550 | [
"Adriana Romero",
"Nicolas Ballas",
"Samira Ebrahimi Kahou",
"Antoine Chassang",
"Carlo Gatta",
"Yoshua Bengio"
] | Poster | null | While depth tends to improve network performances, it also makes
gradient-based training more difficult since deeper networks tend to be more
non-linear. The recently proposed knowledge distillation approach is aimed at
obtaining small and fast-to-execute models, and it has shown that a student
network could imitate ... | [] | null | 12 | 1412.6550 | iclr_archive | [
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Techniques for Learning Binary Stochastic Feedforward Neural Networks | https://arxiv.org/abs/1406.2989 | [
"Tapani Raiko",
"Mathias Berglund",
"Guillaume Alain",
"Laurent Dinh"
] | Poster | null | Stochastic binary hidden units in a multi-layer perceptron (MLP) network give
at least three potential benefits when compared to deterministic MLP networks.
(1) They allow to learn one-to-many type of mappings. (2) They can be used in
structured prediction problems, where modeling the internal structure of the
output... | [] | null | 13 | 1406.2989 | iclr_archive | [
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Reweighted Wake-Sleep | https://arxiv.org/abs/1406.2751 | [
"Jorg Bornschein",
"Yoshua Bengio"
] | Poster | null | Training deep directed graphical models with many hidden variables and
performing inference remains a major challenge. Helmholtz machines and deep
belief networks are such models, and the wake-sleep algorithm has been proposed
to train them. The wake-sleep algorithm relies on training not just the
directed generative... | [] | null | 14 | 1406.2751 | iclr_archive | [
-0.006134308874607086,
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Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs | https://arxiv.org/abs/1412.7062 | [
"Liang-Chieh Chen",
"George Papandreou",
"Iasonas Kokkinos",
"Kevin Murphy",
"Alan Yuille"
] | Poster | null | Deep Convolutional Neural Networks (DCNNs) have recently shown state of the
art performance in high level vision tasks, such as image classification and
object detection. This work brings together methods from DCNNs and
probabilistic graphical models for addressing the task of pixel-level
classification (also called ... | [] | null | 15 | 1412.7062 | iclr_archive | [
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Multiple Object Recognition with Visual Attention | https://arxiv.org/abs/1412.7755 | [
"Jimmy Ba",
"Volodymyr Mnih",
"Koray Kavukcuoglu"
] | Poster | null | We present an attention-based model for recognizing multiple objects in
images. The proposed model is a deep recurrent neural network trained with
reinforcement learning to attend to the most relevant regions of the input
image. We show that the model learns to both localize and recognize multiple
objects despite bei... | [] | null | 16 | 1412.7755 | iclr_archive | [
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0.0... |
Deep Narrow Boltzmann Machines are Universal Approximators | https://arxiv.org/abs/1411.3784 | [
"Guido Montufar"
] | Poster | null | We show that deep narrow Boltzmann machines are universal approximators of
probability distributions on the activities of their visible units, provided
they have sufficiently many hidden layers, each containing the same number of
units as the visible layer. We show that, within certain parameter domains,
deep Boltzma... | [] | null | 17 | 1411.3784 | iclr_archive | [
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... |
Transformation Properties of Learned Visual Representations | https://arxiv.org/abs/1412.7659 | [
"Taco Cohen",
"Max Welling"
] | Poster | null | When a three-dimensional object moves relative to an observer, a change
occurs on the observer's image plane and in the visual representation computed
by a learned model. Starting with the idea that a good visual representation is
one that transforms linearly under scene motions, we show, using the theory of
group re... | [] | null | 18 | 1412.7659 | iclr_archive | [
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0.0... |
Joint RNN-Based Greedy Parsing and Word Composition | https://arxiv.org/abs/1412.7028 | [
"Joël Legrand",
"Ronan Collobert"
] | Poster | null | This paper introduces a greedy parser based on neural networks, which
leverages a new compositional sub-tree representation. The greedy parser and
the compositional procedure are jointly trained, and tightly depends on
each-other. The composition procedure outputs a vector representation which
summarizes syntacticall... | [] | null | 19 | 1412.7028 | iclr_archive | [
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... |
Adam: A Method for Stochastic Optimization | https://arxiv.org/abs/1412.6980 | [
"Jimmy Ba",
"Diederik Kingma"
] | Poster | null | We introduce Adam, an algorithm for first-order gradient-based optimization
of stochastic objective functions, based on adaptive estimates of lower-order
moments. The method is straightforward to implement, is computationally
efficient, has little memory requirements, is invariant to diagonal rescaling
of the gradien... | [] | null | 20 | 1412.6980 | iclr_archive | [
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Neural Machine Translation by Jointly Learning to Align and Translate | https://arxiv.org/abs/1409.0473 | [
"Dzmitry Bahdanau",
"Kyunghyun Cho",
"Yoshua Bengio"
] | Poster | null | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ... | [] | null | 21 | 1409.0473 | iclr_archive | [
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0.00... |
Scheduled denoising autoencoders | https://arxiv.org/abs/1406.3269 | [
"Krzysztof Geras",
"Charles Sutton"
] | Poster | null | We present a representation learning method that learns features at multiple
different levels of scale. Working within the unsupervised framework of
denoising autoencoders, we observe that when the input is heavily corrupted
during training, the network tends to learn coarse-grained features, whereas
when the input i... | [] | null | 22 | 1406.3269 | iclr_archive | [
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... |
Embedding Entities and Relations for Learning and Inference in Knowledge Bases | https://arxiv.org/abs/1412.6575 | [
"Bishan Yang",
"Scott Yih",
"Xiaodong He",
"Jianfeng Gao",
"Li Deng"
] | Poster | null | We consider learning representations of entities and relations in KBs using
the neural-embedding approach. We show that most existing models, including NTN
(Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized
under a unified learning framework, where entities are low-dimensional vectors
learned... | [] | null | 23 | 1412.6575 | iclr_archive | [
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0.0... |
The local low-dimensionality of natural images | https://arxiv.org/abs/1412.6626 | [
"Olivier Henaff",
"Johannes Balle",
"Neil Rabinowitz",
"Eero Simoncelli"
] | Poster | null | We develop a new statistical model for photographic images, in which the
local responses of a bank of linear filters are described as jointly Gaussian,
with zero mean and a covariance that varies slowly over spatial position. We
optimize sets of filters so as to minimize the nuclear norms of matrices of
their local a... | [] | null | 24 | 1412.6626 | iclr_archive | [
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Explaining and Harnessing Adversarial Examples | https://arxiv.org/abs/1412.6572 | [
"Ian Goodfellow",
"Jon Shlens",
"Christian Szegedy"
] | Poster | null | Several machine learning models, including neural networks, consistently
misclassify adversarial examples---inputs formed by applying small but
intentionally worst-case perturbations to examples from the dataset, such that
the perturbed input results in the model outputting an incorrect answer with
high confidence. E... | [] | null | 25 | 1412.6572 | iclr_archive | [
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-0.03261007368564606,
-0.028481515124440193,
-0.020135074853897095,
0.011972925625741482,
-0.06567983329296112,
-... |
Modeling Compositionality with Multiplicative Recurrent Neural Networks | https://arxiv.org/abs/1412.6577 | [
"Ozan Irsoy",
"Claire Cardie"
] | Poster | null | We present the multiplicative recurrent neural network as a general model for
compositional meaning in language, and evaluate it on the task of fine-grained
sentiment analysis. We establish a connection to the previously investigated
matrix-space models for compositionality, and show they are special cases of
the mul... | [] | null | 26 | 1412.6577 | iclr_archive | [
0.005548505578190088,
-0.011056256480515003,
-0.0036014732904732227,
0.036176037043333054,
0.034429922699928284,
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0.01465521939098835,
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-0.010786251164972782,
-0.02656627632677555,
-0.013055704534053802,
-0.0708191767334938,
-0.0... |
Very Deep Convolutional Networks for Large-Scale Image Recognition | https://arxiv.org/abs/1409.1556 | [
"Karen Simonyan",
"Andrew Zisserman"
] | Poster | null | In this work we investigate the effect of the convolutional network depth on
its accuracy in the large-scale image recognition setting. Our main
contribution is a thorough evaluation of networks of increasing depth using an
architecture with very small (3x3) convolution filters, which shows that a
significant improve... | [] | null | 27 | 1409.1556 | iclr_archive | [
0.01817971095442772,
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0.004819185007363558,
0.02936873771250248,
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0.008704915642738342,
0.022175777703523636,
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-0.054987773299217224,
0.0107796099036932,
-0.020215651020407677,
-0.07133769243955612,
0.024413... |
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition | https://arxiv.org/abs/1412.6553 | [
"Vadim Lebedev",
"Yaroslav Ganin",
"Victor Lempitsky",
"Maksim Rakhuba",
"Ivan Oseledets"
] | Poster | null | We propose a simple two-step approach for speeding up convolution layers
within large convolutional neural networks based on tensor decomposition and
discriminative fine-tuning. Given a layer, we use non-linear least squares to
compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a
sum of a sm... | [] | null | 28 | 1412.6553 | iclr_archive | [
-0.00957739818841219,
-0.033649295568466187,
0.013097839429974556,
0.047072310000658035,
0.03745291382074356,
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-0.02201426401734352,
-0.004342054482549429,
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-0.043794501572847366,
-0.02982078678905964,
-0.004548764321953058,
-0.05921582132577896,
0... |
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | https://arxiv.org/abs/1412.6632 | [
"Junhua Mao",
"Wei Xu",
"Yi Yang",
"Jiang Wang",
"Alan Yuille"
] | Poster | null | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-netw... | [] | null | 29 | 1412.6632 | iclr_archive | [
-0.009589101187884808,
-0.041977301239967346,
-0.015281761065125465,
0.07077564299106598,
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0.0399647057056427,
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-0.01798888109624386,
-0.03508518263697624,
0.029002578929066658,
-0.05790695920586586,
-0.0007... |
Deep Structured Output Learning for Unconstrained Text Recognition | https://arxiv.org/abs/1412.5903 | [
"Max Jaderberg",
"Karen Simonyan",
"Andrea Vedaldi",
"Andrew Zisserman"
] | Poster | null | We develop a representation suitable for the unconstrained recognition of
words in natural images: the general case of no fixed lexicon and unknown
length.
To this end we propose a convolutional neural network (CNN) based
architecture which incorporates a Conditional Random Field (CRF) graphical
model, taking the w... | [] | null | 30 | 1412.5903 | iclr_archive | [
-0.00413596211001277,
-0.027735279873013496,
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0.0515449196100235,
0.033686406910419464,
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-0.02687574177980423,
-0.027515815570950508,
0.033219192177057266,
-0.0742066279053688,
-0... |
Zero-bias autoencoders and the benefits of co-adapting features | https://arxiv.org/abs/1402.3337 | [
"Kishore Konda",
"Roland Memisevic",
"David Krueger"
] | Poster | null | Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representa... | [] | null | 31 | 1402.3337 | iclr_archive | [
-0.003283413592725992,
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0.022366123273968697,
-0.08072246611118317,
0... |
Automatic Discovery and Optimization of Parts for Image Classification | https://arxiv.org/abs/1412.6598 | [
"Sobhan Naderi Parizi",
"Andrea Vedaldi",
"Andrew Zisserman",
"Pedro Felzenszwalb"
] | Poster | null | Part-based representations have been shown to be very useful for image
classification. Learning part-based models is often viewed as a two-stage
problem. First, a collection of informative parts is discovered, using
heuristics that promote part distinctiveness and diversity, and then
classifiers are trained on the ve... | [] | null | 32 | 1412.6598 | iclr_archive | [
0.015468486584722996,
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0.0019417139701545238,
0.049846913665533066,
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-0.033994968980550766,
-0.056602105498313904,
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-0.055353425443172455,
... |
Understanding Locally Competitive Networks | https://arxiv.org/abs/1410.1165 | [
"Rupesh Srivastava",
"Jonathan Masci",
"Faustino Gomez",
"Juergen Schmidhuber"
] | Poster | null | Recently proposed neural network activation functions such as rectified
linear, maxout, and local winner-take-all have allowed for faster and more
effective training of deep neural architectures on large and complex datasets.
The common trait among these functions is that they implement local competition
between smal... | [] | null | 33 | 1410.1165 | iclr_archive | [
-0.04629231616854668,
-0.02736620046198368,
-0.014420234598219395,
0.03352563455700874,
0.04616902768611908,
0.03078104555606842,
0.004144294187426567,
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-0.020129309967160225,
0.026681847870349884,
0.00629772013053298,
-0.050186313688755035,
-0.... |
Leveraging Monolingual Data for Crosslingual Compositional Word Representations | https://arxiv.org/abs/1412.6334 | [
"Hubert Soyer",
"Pontus Stenetorp",
"Akiko Aizawa"
] | Poster | null | In this work, we present a novel neural network based architecture for
inducing compositional crosslingual word representations. Unlike previously
proposed methods, our method fulfills the following three criteria; it
constrains the word-level representations to be compositional, it is capable of
leveraging both bili... | [] | null | 34 | 1412.6334 | iclr_archive | [
-0.004371400456875563,
-0.011180587112903595,
0.006720326840877533,
0.03989139944314957,
0.021813038736581802,
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0.00294155883602798,
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-0.021311862394213676,
-0.019789498299360275,
0.010546883568167686,
-0.0493544302880764,
-0... |
Move Evaluation in Go Using Deep Convolutional Neural Networks | https://arxiv.org/abs/1412.6564 | [
"Chris Maddison",
"Aja Huang",
"Ilya Sutskever",
"David Silver"
] | Poster | null | The game of Go is more challenging than other board games, due to the
difficulty of constructing a position or move evaluation function. In this
paper we investigate whether deep convolutional networks can be used to
directly represent and learn this knowledge. We train a large 12-layer
convolutional neural network b... | [] | null | 35 | 1412.6564 | iclr_archive | [
-0.03206796199083328,
-0.049488164484500885,
0.00029614329105243087,
0.042780227959156036,
0.045022062957286835,
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-0.036135800182819366,
0.01486896350979805,
-0.005711401347070932,
-0.06853080540895462,
... |
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation | https://arxiv.org/abs/1412.7580 | [
"Nicolas Vasilache",
"Jeff Johnson",
"Michael Mathieu",
"Soumith Chintala",
"Serkan Piantino",
"Yann LeCun"
] | Poster | null | We examine the performance profile of Convolutional Neural Network training
on the current generation of NVIDIA Graphics Processing Units. We introduce two
new Fast Fourier Transform convolution implementations: one based on NVIDIA's
cuFFT library, and another based on a Facebook authored FFT implementation,
fbfft, t... | [] | null | 36 | 1412.7580 | iclr_archive | [
0.005991718731820583,
-0.04726449027657509,
0.025601275265216827,
0.025315532460808754,
0.03517220914363861,
0.03579822927713394,
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0.049497686326503754,
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-0.05632399022579193,
0.015573403798043728,
0.0014487336156889796,
-0.07538507133722305,
-0.... |
Word Representations via Gaussian Embedding | https://arxiv.org/abs/1412.6623 | [
"Luke Vilnis",
"Andrew McCallum"
] | Poster | null | Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product ... | [] | null | 37 | 1412.6623 | iclr_archive | [
-0.022525973618030548,
-0.007958680391311646,
0.0029189216438680887,
0.06130314990878105,
0.026897192001342773,
0.05592765286564827,
0.02717588283121586,
0.0026656747795641422,
-0.002198993694037199,
-0.03068809024989605,
-0.009585809893906116,
0.015337381511926651,
-0.07212560623884201,
0... |
Qualitatively characterizing neural network optimization problems | https://arxiv.org/abs/1412.6544 | [
"Ian Goodfellow",
"Oriol Vinyals"
] | Poster | null | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
a... | [] | null | 38 | 1412.6544 | iclr_archive | [
-0.04026986286044121,
-0.022642264142632484,
-0.005983470473438501,
0.04811333492398262,
0.03697055205702782,
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-0.03219089284539223,
-0.019967157393693924,
0.022246871143579483,
-0.0397377647459507,
0... |
Memory Networks | https://arxiv.org/abs/1410.3916 | [
"Jason Weston",
"Sumit Chopra",
"Antoine Bordes"
] | Poster | null | We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in... | [] | null | 39 | 1410.3916 | iclr_archive | [
-0.009670330211520195,
0.001341706607490778,
-0.009222445078194141,
0.0511188805103302,
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0.010568782687187195,
0.0015729879960417747,
0.024641918018460274,
-0.04645440727472305,
-0.01... |
Generative Modeling of Convolutional Neural Networks | https://arxiv.org/abs/1412.6296 | [
"Jifeng Dai",
"Yang Lu",
"Ying-Nian Wu"
] | Poster | null | The convolutional neural networks (CNNs) have proven to be a powerful tool
for discriminative learning. Recently researchers have also started to show
interest in the generative aspects of CNNs in order to gain a deeper
understanding of what they have learned and how to further improve them. This
paper investigates g... | [] | null | 40 | 1412.6296 | iclr_archive | [
0.021600598469376564,
-0.023329369723796844,
-0.0077382950112223625,
0.06096671521663666,
0.024356914684176445,
0.038858845829963684,
-0.01665736362338066,
0.013275419361889362,
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-0.05039505288004875,
-0.013955784030258656,
-0.02049321122467518,
-0.059901464730501175,
... |
A Unified Perspective on Multi-Domain and Multi-Task Learning | https://arxiv.org/abs/1412.7489 | [
"Yongxin Yang",
"Timothy Hospedales"
] | Poster | null | In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different... | [] | null | 41 | 1412.7489 | iclr_archive | [
0.008415445685386658,
0.0007903411169536412,
-0.015737637877464294,
0.04130909591913223,
0.03519966080784798,
0.03630171716213226,
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0.004557198844850063,
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-0.020902160555124283,
-0.023371919989585876,
0.037543702870607376,
-0.0780433863401413,
-0.00... |
Object detectors emerge in Deep Scene CNNs | https://arxiv.org/abs/1412.6856 | [
"Bolei Zhou",
"Aditya Khosla",
"Agata Lapedriza",
"Aude Oliva",
"Antonio Torralba"
] | Poster | null | With the success of new computational architectures for visual processing,
such as convolutional neural networks (CNN) and access to image databases with
millions of labeled examples (e.g., ImageNet, Places), the state of the art in
computer vision is advancing rapidly. One important factor for continued
progress is ... | [] | null | 42 | 1412.6856 | iclr_archive | [
-0.006357009056955576,
0.001259227399714291,
0.02100524492561817,
0.04599931463599205,
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0.010036276653409004,
0.01003933697938919,
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-0.03792363777756691,
-0.03891061991453171,
0.006014237646013498,
-0.05411538854241371,
-0.002... |
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