title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|
Multi-Scale Context Aggregation by Dilated Convolutions | https://arxiv.org/abs/1511.07122 | [
"Fisher Yu",
"Vladlen Koltun"
] | null | null | State-of-the-art models for semantic segmentation are based on adaptations of
convolutional networks that had originally been designed for image
classification. However, dense prediction and image classification are
structurally different. In this work, we develop a new convolutional network
module that is specifical... | [] | null | 1 | 1511.07122 | iclr_archive | [
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The Variational Fair Autoencoder | https://arxiv.org/abs/1511.00830 | [
"Christos Louizos",
"Kevin Swersky",
"Yujia Li",
"Max Welling",
"Richard Zemel"
] | null | null | We investigate the problem of learning representations that are invariant to
certain nuisance or sensitive factors of variation in the data while retaining
as much of the remaining information as possible. Our model is based on a
variational autoencoding architecture with priors that encourage independence
between se... | [] | null | 2 | 1511.00830 | iclr_archive | [
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A note on the evaluation of generative models | https://arxiv.org/abs/1511.01844 | [
"Lucas Theis",
"Aäron van den Oord",
"Matthias Bethge"
] | null | null | Probabilistic generative models can be used for compression, denoising,
inpainting, texture synthesis, semi-supervised learning, unsupervised feature
learning, and other tasks. Given this wide range of applications, it is not
surprising that a lot of heterogeneity exists in the way these models are
formulated, traine... | [] | null | 3 | 1511.01844 | iclr_archive | [
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Learning to Diagnose with LSTM Recurrent Neural Networks | https://arxiv.org/abs/1511.03677 | [
"Zachary Lipton",
"David Kale",
"Charles Elkan",
"Randall Wetzel"
] | null | null | Clinical medical data, especially in the intensive care unit (ICU), consist
of multivariate time series of observations. For each patient visit (or
episode), sensor data and lab test results are recorded in the patient's
Electronic Health Record (EHR). While potentially containing a wealth of
insights, the data is di... | [] | null | 4 | 1511.03677 | iclr_archive | [
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Prioritized Experience Replay | https://arxiv.org/abs/1511.05952 | [
"Tom Schaul",
"John Quan",
"Ioannis Antonoglou",
"David Silver"
] | null | null | Experience replay lets online reinforcement learning agents remember and
reuse experiences from the past. In prior work, experience transitions were
uniformly sampled from a replay memory. However, this approach simply replays
transitions at the same frequency that they were originally experienced,
regardless of thei... | [] | null | 5 | 1511.05952 | iclr_archive | [
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Importance Weighted Autoencoders | https://arxiv.org/abs/1509.00519 | [
"Yuri Burda",
"Ruslan Salakhutdinov",
"Roger Grosse"
] | null | null | The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently
proposed generative model pairing a top-down generative network with a
bottom-up recognition network which approximates posterior inference. It
typically makes strong assumptions about posterior inference, for instance that
the posterior distribu... | [] | null | 6 | 1509.00519 | iclr_archive | [
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding | https://arxiv.org/abs/1510.00149 | [
"Song Han",
"Huizi Mao",
"Bill Dally"
] | null | null | Neural networks are both computationally intensive and memory intensive,
making them difficult to deploy on embedded systems with limited hardware
resources. To address this limitation, we introduce "deep compression", a three
stage pipeline: pruning, trained quantization and Huffman coding, that work
together to red... | [] | null | 7 | 1510.00149 | iclr_archive | [
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Variationally Auto-Encoded Deep Gaussian Processes | https://arxiv.org/abs/1511.06455 | [
"Zhenwen Dai",
"Andreas Damianou",
"Javier Gonzalez",
"Neil Lawrence"
] | null | null | We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformula... | [] | null | 8 | 1511.06455 | iclr_archive | [
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Training Convolutional Neural Networks with Low-rank Filters for Efficient Image Classification | https://arxiv.org/abs/1511.06744 | [
"Yani Ioannou",
"Duncan Robertson",
"Jamie Shotton",
"roberto Cipolla",
"Antonio Criminisi",
"Jamie Shotton"
] | null | null | We propose a new method for creating computationally efficient convolutional
neural networks (CNNs) by using low-rank representations of convolutional
filters. Rather than approximating filters in previously-trained networks with
more efficient versions, we learn a set of small basis filters from scratch;
during trai... | [] | null | 9 | 1511.06744 | iclr_archive | [
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Neural Networks with Few Multiplications | https://arxiv.org/abs/1510.03009 | [
"Zhouhan Lin",
"Matthieu Courbariaux",
"Roland Memisevic",
"Yoshua Bengio"
] | null | null | For most deep learning algorithms training is notoriously time consuming.
Since most of the computation in training neural networks is typically spent on
floating point multiplications, we investigate an approach to training that
eliminates the need for most of these. Our method consists of two parts: First
we stocha... | [] | null | 10 | 1510.03009 | iclr_archive | [
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Reducing Overfitting in Deep Networks by Decorrelating Representations | https://arxiv.org/abs/1511.06068 | [
"Michael Cogswell",
"Faruk Ahmed",
"Ross Girshick",
"Larry Zitnick",
"Dhruv Batra"
] | null | null | One major challenge in training Deep Neural Networks is preventing
overfitting. Many techniques such as data augmentation and novel regularizers
such as Dropout have been proposed to prevent overfitting without requiring a
massive amount of training data. In this work, we propose a new regularizer
called DeCov which ... | [] | null | 11 | 1511.06068 | iclr_archive | [
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0.01... |
Pushing the Boundaries of Boundary Detection using Deep Learning | https://arxiv.org/abs/1511.07386 | [
"Iasonas Kokkinos"
] | null | null | In this work we show that adapting Deep Convolutional Neural Network training
to the task of boundary detection can result in substantial improvements over
the current state-of-the-art in boundary detection.
Our contributions consist firstly in combining a careful design of the loss
for boundary detection training,... | [] | null | 12 | 1511.07386 | iclr_archive | [
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Generating Images from Captions with Attention | https://arxiv.org/abs/1511.02793 | [
"Elman Mansimov",
"Emilio Parisotto",
"Jimmy Ba",
"Ruslan Salakhutdinov"
] | null | null | Motivated by the recent progress in generative models, we introduce a model
that generates images from natural language descriptions. The proposed model
iteratively draws patches on a canvas, while attending to the relevant words in
the description. After training on Microsoft COCO, we compare our model with
several ... | [] | null | 13 | 1511.02793 | iclr_archive | [
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Reasoning about Entailment with Neural Attention | https://arxiv.org/abs/1509.06664 | [
"Tim Rocktäschel",
"Edward Grefenstette",
"Karl Moritz Hermann",
"Tomáš Kočiský",
"Phil Blunsom"
] | null | null | While most approaches to automatically recognizing entailment relations have
used classifiers employing hand engineered features derived from complex
natural language processing pipelines, in practice their performance has been
only slightly better than bag-of-word pair classifiers using only lexical
similarity. The ... | [] | null | 14 | 1509.06664 | iclr_archive | [
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Convolutional Neural Networks With Low-rank Regularization | https://arxiv.org/abs/1511.06067 | [
"Cheng Tai",
"Tong Xiao",
"Yi Zhang",
"Xiaogang Wang",
"Weinan E"
] | null | null | Large CNNs have delivered impressive performance in various computer vision
applications. But the storage and computation requirements make it problematic
for deploying these models on mobile devices. Recently, tensor decompositions
have been used for speeding up CNNs. In this paper, we further develop the
tensor dec... | [] | null | 15 | 1511.06067 | iclr_archive | [
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Unifying distillation and privileged information | https://arxiv.org/abs/1511.03643 | [
"David Lopez-Paz",
"Leon Bottou",
"Bernhard Schölkopf",
"Vladimir Vapnik"
] | null | null | Distillation (Hinton et al., 2015) and privileged information (Vapnik &
Izmailov, 2015) are two techniques that enable machines to learn from other
machines. This paper unifies these two techniques into generalized
distillation, a framework to learn from multiple machines and data
representations. We provide theoreti... | [] | null | 16 | 1511.03643 | iclr_archive | [
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Particular object retrieval with integral max-pooling of CNN activations | https://arxiv.org/abs/1511.05879 | [
"[code] Giorgos Tolias",
"Ronan Sicre",
"Hervé Jégou"
] | null | null | Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some parti... | [] | null | 17 | 1511.05879 | iclr_archive | [
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All you need is a good init | https://arxiv.org/abs/1511.06422 | [
"[code] Dmytro Mishkin",
"Jiri Matas"
] | null | null | Layer-sequential unit-variance (LSUV) initialization - a simple method for
weight initialization for deep net learning - is proposed. The method consists
of the two steps. First, pre-initialize weights of each convolution or
inner-product layer with orthonormal matrices. Second, proceed from the first
to the final la... | [] | null | 18 | 1511.06422 | iclr_archive | [
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Bayesian Representation Learning with Oracle Constraints | https://arxiv.org/abs/1506.05011 | [
"Theofanis Karaletsos",
"Serge Belongie",
"Gunnar Rätsch"
] | null | null | Representation learning systems typically rely on massive amounts of labeled
data in order to be trained to high accuracy. Recently, high-dimensional
parametric models like neural networks have succeeded in building rich
representations using either compressive, reconstructive or supervised
criteria. However, the sem... | [] | null | 19 | 1506.05011 | iclr_archive | [
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Neural Programmer: Inducing Latent Programs with Gradient Descent | https://arxiv.org/abs/1511.04834 | [
"Arvind Neelakantan",
"Quoc Le",
"Ilya Sutskever"
] | null | null | Deep neural networks have achieved impressive supervised classification
performance in many tasks including image recognition, speech recognition, and
sequence to sequence learning. However, this success has not been translated to
applications like question answering that may involve complex arithmetic and
logic reas... | [] | null | 20 | 1511.04834 | iclr_archive | [
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Towards Universal Paraphrastic Sentence Embeddings | https://arxiv.org/abs/1511.08198 | [
"[code] John Wieting",
"Mohit Bansal",
"Kevin Gimpel",
"Karen Livescu"
] | null | null | We consider the problem of learning general-purpose, paraphrastic sentence
embeddings based on supervision from the Paraphrase Database (Ganitkevitch et
al., 2013). We compare six compositional architectures, evaluating them on
annotated textual similarity datasets drawn both from the same distribution as
the trainin... | [] | null | 21 | 1511.08198 | iclr_archive | [
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Regularizing RNNs by Stabilizing Activations | https://arxiv.org/abs/1511.08400 | [
"David Krueger",
"Roland Memisevic"
] | null | null | We stabilize the activations of Recurrent Neural Networks (RNNs) by
penalizing the squared distance between successive hidden states' norms.
This penalty term is an effective regularizer for RNNs including LSTMs and
IRNNs, improving performance on character-level language modeling and phoneme
recognition, and outpe... | [] | null | 22 | 1511.08400 | iclr_archive | [
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SparkNet: Training Deep Networks in Spark | https://arxiv.org/abs/1511.06051 | [
"Philipp Moritz",
"Robert Nishihara",
"Ion Stoica",
"Michael Jordan"
] | null | null | Training deep networks is a time-consuming process, with networks for object
recognition often requiring multiple days to train. For this reason, leveraging
the resources of a cluster to speed up training is an important area of work.
However, widely-popular batch-processing computational frameworks like
MapReduce an... | [] | null | 23 | 1511.06051 | iclr_archive | [
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... |
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks | https://arxiv.org/abs/1511.06390 | [
"Jost Tobias Springenberg"
] | null | null | In this paper we present a method for learning a discriminative classifier
from unlabeled or partially labeled data. Our approach is based on an objective
function that trades-off mutual information between observed examples and their
predicted categorical class distribution, against robustness of the classifier
to a... | [] | null | 24 | 1511.06390 | iclr_archive | [
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The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations | https://arxiv.org/abs/1511.02301 | [
"Felix Hill",
"Antoine Bordes",
"Sumit Chopra",
"Jason Weston"
] | null | null | We introduce a new test of how well language models capture meaning in
children's books. Unlike standard language modelling benchmarks, it
distinguishes the task of predicting syntactic function words from that of
predicting lower-frequency words, which carry greater semantic content. We
compare a range of state-of-t... | [] | null | 25 | 1511.02301 | iclr_archive | [
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0.013029864057898521,
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0... |
MuProp: Unbiased Backpropagation For Stochastic Neural Networks | https://arxiv.org/abs/1511.05176 | [
"Shixiang Gu",
"Sergey Levine",
"Ilya Sutskever",
"Andriy Mnih"
] | null | null | Deep neural networks are powerful parametric models that can be trained
efficiently using the backpropagation algorithm. Stochastic neural networks
combine the power of large parametric functions with that of graphical models,
which makes it possible to learn very complex distributions. However, as
backpropagation is... | [] | null | 26 | 1511.05176 | iclr_archive | [
-0.008721927180886269,
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Data Representation and Compression Using Linear-Programming Approximations | https://arxiv.org/abs/1511.06606 | [
"Hristo Paskov",
"John Mitchell",
"Trevor Hastie"
] | null | null | We propose `Dracula', a new framework for unsupervised feature selection from
sequential data such as text. Dracula learns a dictionary of $n$-grams that
efficiently compresses a given corpus and recursively compresses its own
dictionary; in effect, Dracula is a `deep' extension of Compressive Feature
Learning. It re... | [] | null | 27 | 1511.06606 | iclr_archive | [
-0.020966369658708572,
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-... |
Diversity Networks | https://arxiv.org/abs/1511.05077 | [
"Zelda Mariet",
"Suvrit Sra"
] | null | null | We introduce Divnet, a flexible technique for learning networks with diverse
neurons. Divnet models neuronal diversity by placing a Determinantal Point
Process (DPP) over neurons in a given layer. It uses this DPP to select a
subset of diverse neurons and subsequently fuses the redundant neurons into the
selected one... | [] | null | 28 | 1511.05077 | iclr_archive | [
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Deep Reinforcement Learning in Parameterized Action Space | https://arxiv.org/abs/1511.04143 | [
"[code] [data] Matthew Hausknecht",
"Peter Stone"
] | null | null | Recent work has shown that deep neural networks are capable of approximating
both value functions and policies in reinforcement learning domains featuring
continuous state and action spaces. However, to the best of our knowledge no
previous work has succeeded at using deep neural networks in structured
(parameterized... | [] | null | 29 | 1511.04143 | iclr_archive | [
-0.03070882335305214,
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Learning VIsual Predictive Models of Physics for Playing Billiards | https://arxiv.org/abs/1511.07404 | [
"Katerina Fragkiadaki",
"Pulkit Agrawal",
"Sergey Levine",
"Jitendra Malik"
] | null | null | The ability to plan and execute goal specific actions in varied, unexpected
settings is a central requirement of intelligent agents. In this paper, we
explore how an agent can be equipped with an internal model of the dynamics of
the external world, and how it can use this model to plan novel actions by
running multi... | [] | null | 30 | 1511.07404 | iclr_archive | [
-0.03584618121385574,
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-... |
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | https://arxiv.org/abs/1502.05698 | [
"[code] [data] Jason Weston",
"Antoine Bordes",
"Sumit Chopra",
"Sasha Rush",
"Bart van Merrienboer",
"Armand Joulin",
"Tomas Mikolov"
] | null | null | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question... | [] | null | 31 | 1502.05698 | iclr_archive | [
-0.007688414771109819,
-0.019020214676856995,
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0.008192448876798153,
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0.07557123154401779,
-0.05022421479225159,
-0... |
Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems | https://arxiv.org/abs/1511.06931 | [
"[data] Jesse Dodge",
"Andreea Gane",
"Xiang Zhang",
"Antoine Bordes",
"Sumit Chopra",
"Alexander Miller",
"Arthur Szlam",
"Jason Weston"
] | null | null | A long-term goal of machine learning is to build intelligent conversational
agents. One recent popular approach is to train end-to-end models on a large
amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals
& Le, 2015; Shang et al., 2015). However, this approach leaves many questions
unanswe... | [] | null | 32 | 1511.06931 | iclr_archive | [
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0.08708975464105606,
-0.054042380303144455,
-... |
Better Computer Go Player with Neural Network and Long-term Prediction | https://arxiv.org/abs/1511.06410 | [
"Yuandong Tian",
"Yan Zhu"
] | null | null | Competing with top human players in the ancient game of Go has been a
long-term goal of artificial intelligence. Go's high branching factor makes
traditional search techniques ineffective, even on leading-edge hardware, and
Go's evaluation function could change drastically with one stone change. Recent
works [Maddiso... | [] | null | 33 | 1511.06410 | iclr_archive | [
-0.036803536117076874,
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Distributional Smoothing with Virtual Adversarial Training | https://arxiv.org/abs/1507.00677 | [
"[code] Takeru Miyato",
"Shin-ichi Maeda",
"Masanori Koyama",
"Ken Nakae",
"Shin Ishii"
] | null | null | We propose local distributional smoothness (LDS), a new notion of smoothness
for statistical model that can be used as a regularization term to promote the
smoothness of the model distribution. We named the LDS based regularization as
virtual adversarial training (VAT). The LDS of a model at an input datapoint is
def... | [] | null | 34 | 1507.00677 | iclr_archive | [
0.02795182541012764,
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0.01... |
Multi-task Sequence to Sequence Learning | https://arxiv.org/abs/1511.06114 | [
"Minh-Thang Luong",
"Quoc Le",
"Ilya Sutskever",
"Oriol Vinyals",
"Lukasz Kaiser"
] | null | null | Sequence to sequence learning has recently emerged as a new paradigm in
supervised learning. To date, most of its applications focused on only one task
and not much work explored this framework for multiple tasks. This paper
examines three multi-task learning (MTL) settings for sequence to sequence
models: (a) the on... | [] | null | 35 | 1511.06114 | iclr_archive | [
0.03358972817659378,
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A Test of Relative Similarity for Model Selection in Generative Models | https://arxiv.org/abs/1511.04581 | [
"Eugene Belilovsky",
"Wacha Bounliphone",
"Matthew Blaschko",
"Ioannis Antonoglou",
"Arthur Gretton"
] | null | null | Probabilistic generative models provide a powerful framework for representing
data that avoids the expense of manual annotation typically needed by
discriminative approaches. Model selection in this generative setting can be
challenging, however, particularly when likelihoods are not easily accessible.
To address thi... | [] | null | 36 | 1511.04581 | iclr_archive | [
-0.010373447090387344,
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... |
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications | https://arxiv.org/abs/1511.06530 | [
"Yong-Deok Kim",
"Eunhyeok Park",
"Sungjoo Yoo",
"Taelim Choi",
"Lu Yang",
"Dongjun Shin"
] | null | null | Although the latest high-end smartphone has powerful CPU and GPU, running
deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet
classification on mobile devices is challenging. To deploy deep CNNs on mobile
devices, we present a simple and effective scheme to compress the entire CNN,
which we... | [] | null | 37 | 1511.06530 | iclr_archive | [
-0.017092199996113777,
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-... |
Neural Programmer-Interpreters | https://arxiv.org/abs/1511.06279 | [
"Scott Reed",
"Nando de Freitas"
] | null | null | We propose the neural programmer-interpreter (NPI): a recurrent and
compositional neural network that learns to represent and execute programs. NPI
has three learnable components: a task-agnostic recurrent core, a persistent
key-value program memory, and domain-specific encoders that enable a single NPI
to operate in... | [] | null | 38 | 1511.06279 | iclr_archive | [
-0.017024751752614975,
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0.022130975499749184,
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... |
Session-based recommendations with recurrent neural networks | https://arxiv.org/abs/1511.06939 | [
"[code] Balázs Hidasi",
"Alexandros Karatzoglou",
"Linas Baltrunas",
"Domonkos Tikk"
] | null | null | We apply recurrent neural networks (RNN) on a new domain, namely recommender
systems. Real-life recommender systems often face the problem of having to base
recommendations only on short session-based data (e.g. a small sportsware
website) instead of long user histories (as in the case of Netflix). In this
situation ... | [] | null | 39 | 1511.06939 | iclr_archive | [
0.015392093919217587,
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0.019254222512245178,
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-0.... |
Continuous control with deep reinforcement learning | https://arxiv.org/abs/1509.02971 | [
"Timothy Lillicrap",
"Jonathan Hunt",
"Alexander Pritzel",
"Nicolas Heess",
"Tom Erez",
"Yuval Tassa",
"David Silver",
"Daan Wierstra"
] | null | null | We adapt the ideas underlying the success of Deep Q-Learning to the
continuous action domain. We present an actor-critic, model-free algorithm
based on the deterministic policy gradient that can operate over continuous
action spaces. Using the same learning algorithm, network architecture and
hyper-parameters, our al... | [] | null | 40 | 1509.02971 | iclr_archive | [
-0.02387125790119171,
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0.018074598163366318,
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-0.... |
Recurrent Gaussian Processes | https://arxiv.org/abs/1511.06644 | [
"César Lincoln Mattos",
"Zhenwen Dai",
"Andreas Damianou",
"Jeremy Forth",
"Guilherme Barreto",
"Neil Lawrence"
] | null | null | We define Recurrent Gaussian Processes (RGP) models, a general family of
Bayesian nonparametric models with recurrent GP priors which are able to learn
dynamical patterns from sequential data. Similar to Recurrent Neural Networks
(RNNs), RGPs can have different formulations for their internal states,
distinct inferen... | [] | null | 41 | 1511.06644 | iclr_archive | [
0.01981847919523716,
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-0.06524926424026489,
-0.01280... |
Modeling Visual Representations:Defining Properties and Deep Approximations | https://arxiv.org/abs/1411.7676 | [
"Stefano Soatto",
"Alessandro Chiuso"
] | null | null | Visual representations are defined in terms of minimal sufficient statistics
of visual data, for a class of tasks, that are also invariant to nuisance
variability. Minimal sufficiency guarantees that we can store a representation
in lieu of raw data with smallest complexity and no performance loss on the
task at hand... | [] | null | 42 | 1411.7676 | iclr_archive | [
0.007112422958016396,
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0.0... |
Auxiliary Image Regularization for Deep CNNs with Noisy Labels | https://arxiv.org/abs/1511.07069 | [
"Samaneh Azadi",
"Jiashi Feng",
"Stefanie Jegelka",
"Trevor Darrell"
] | null | null | Precisely-labeled data sets with sufficient amount of samples are very
important for training deep convolutional neural networks (CNNs). However, many
of the available real-world data sets contain erroneously labeled samples and
those errors substantially hinder the learning of very accurate CNN models. In
this work,... | [] | null | 43 | 1511.07069 | iclr_archive | [
0.013181847520172596,
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0.0031764593441039324,
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-... |
Convergent Learning: Do different neural networks learn the same representations? | https://arxiv.org/abs/1511.07543 | [
"Yixuan Li",
"Jason Yosinski",
"Jeff Clune",
"Hod Lipson",
"John Hopcroft"
] | null | null | Recent success in training deep neural networks have prompted active
investigation into the features learned on their intermediate layers. Such
research is difficult because it requires making sense of non-linear
computations performed by millions of parameters, but valuable because it
increases our ability to unders... | [] | null | 44 | 1511.07543 | iclr_archive | [
-0.021159542724490166,
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0.0064898827113211155,
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0.... |
Policy Distillation | https://arxiv.org/abs/1511.06295 | [
"Andrei Rusu",
"Sergio Gomez",
"Caglar Gulcehre",
"Guillaume Desjardins",
"James Kirkpatrick",
"Razvan Pascanu",
"Volodymyr Mnih",
"Koray Kavukcuoglu",
"Raia Hadsell"
] | null | null | Policies for complex visual tasks have been successfully learned with deep
reinforcement learning, using an approach called deep Q-networks (DQN), but
relatively large (task-specific) networks and extensive training are needed to
achieve good performance. In this work, we present a novel method called policy
distilla... | [] | null | 45 | 1511.06295 | iclr_archive | [
0.00410880520939827,
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0.008876219391822815,
-0.08448699861764908,
-0.0... |
Neural Random-Access Machines | https://arxiv.org/abs/1511.06392 | [
"Karol Kurach",
"Marcin Andrychowicz",
"Ilya Sutskever"
] | null | null | In this paper, we propose and investigate a new neural network architecture
called Neural Random Access Machine. It can manipulate and dereference pointers
to an external variable-size random-access memory. The model is trained from
pure input-output examples using backpropagation.
We evaluate the new model on a nu... | [] | null | 46 | 1511.06392 | iclr_archive | [
-0.017138520255684853,
0.030431680381298065,
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0.0009491952951066196,
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-0.0509425587952137,
-0.0... |
Gated Graph Sequence Neural Networks | https://arxiv.org/abs/1511.05493 | [
"Yujia Li",
"Daniel Tarlow",
"Marc Brockschmidt",
"Richard Zemel",
"CIFAR"
] | null | null | Graph-structured data appears frequently in domains including chemistry,
natural language semantics, social networks, and knowledge bases. In this work,
we study feature learning techniques for graph-structured inputs. Our starting
point is previous work on Graph Neural Networks (Scarselli et al., 2009), which
we mod... | [] | null | 47 | 1511.05493 | iclr_archive | [
-0.014521097764372826,
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0.02683348022401333,
0.004192591179162264,
-0.0709376409649849,
0.003291... |
Metric Learning with Adaptive Density Discrimination | https://arxiv.org/abs/1511.05939 | [
"Oren Rippel",
"Manohar Paluri",
"Piotr Dollar",
"Lubomir Bourdev"
] | null | null | Distance metric learning (DML) approaches learn a transformation to a
representation space where distance is in correspondence with a predefined
notion of similarity. While such models offer a number of compelling benefits,
it has been difficult for these to compete with modern classification
algorithms in performanc... | [] | null | 48 | 1511.05939 | iclr_archive | [
0.007872364483773708,
0.008405604399740696,
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0.0063105057924985886,
-0.08364470303058624,
... |
Censoring Representations with an Adversary | https://arxiv.org/abs/1511.05897 | [
"Harrison Edwards",
"Amos Storkey"
] | null | null | In practice, there are often explicit constraints on what representations or
decisions are acceptable in an application of machine learning. For example it
may be a legal requirement that a decision must not favour a particular group.
Alternatively it can be that that representation of data must not have
identifying ... | [] | null | 49 | 1511.05897 | iclr_archive | [
-0.009603838436305523,
-0.015149622224271297,
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0... |
Order-Embeddings of Images and Language | https://arxiv.org/abs/1511.06361 | [
"[code] Ivan Vendrov",
"Ryan Kiros",
"Sanja Fidler",
"Raquel Urtasun"
] | null | null | Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning order... | [] | null | 50 | 1511.06361 | iclr_archive | [
-0.031282585114240646,
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0.035492245107889175,
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Variable Rate Image Compression with Recurrent Neural Networks | https://arxiv.org/abs/1511.06085 | [
"George Toderici",
"Sean O'Malley",
"Damien Vincent",
"Sung Jin Hwang",
"Michele Covell",
"Shumeet Baluja",
"Rahul Sukthankar",
"David Minnen"
] | null | null | A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of ... | [] | null | 51 | 1511.06085 | iclr_archive | [
0.0026713982224464417,
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0.... |
Delving Deeper into Convolutional Networks for Learning Video Representations | https://arxiv.org/abs/1511.06432 | [
"Nicolas Ballas",
"Li Yao",
"Pal Chris",
"Aaron Courville"
] | null | null | We propose an approach to learn spatio-temporal features in videos from
intermediate visual representations we call "percepts" using
Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts
that are extracted from all level of a deep convolutional network trained on
the large ImageNet dataset. Whi... | [] | null | 52 | 1511.06432 | iclr_archive | [
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0.0... |
8-Bit Approximations for Parallelism in Deep Learning | https://arxiv.org/abs/1511.04561 | [
"Tim Dettmers"
] | null | null | The creation of practical deep learning data-products often requires
parallelization across processors and computers to make deep learning feasible
on large data sets, but bottlenecks in communication bandwidth make it
difficult to attain good speedups through parallelism. Here we develop and test
8-bit approximation... | [] | null | 53 | 1511.04561 | iclr_archive | [
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0.01926... |
Data-dependent initializations of Convolutional Neural Networks | https://arxiv.org/abs/1511.06856 | [
"[code] Philipp Kraehenbuehl",
"Carl Doersch",
"Jeff Donahue",
"Trevor Darrell"
] | null | null | Convolutional Neural Networks spread through computer vision like a wildfire,
impacting almost all visual tasks imaginable. Despite this, few researchers
dare to train their models from scratch. Most work builds on one of a handful
of ImageNet pre-trained models, and fine-tunes or adapts these for specific
tasks. Thi... | [] | null | 54 | 1511.06856 | iclr_archive | [
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Order Matters: Sequence to sequence for sets | https://arxiv.org/abs/1511.06391 | [
"Oriol Vinyals",
"Samy Bengio",
"Manjunath Kudlur"
] | null | null | Sequences have become first class citizens in supervised learning thanks to
the resurgence of recurrent neural networks. Many complex tasks that require
mapping from or to a sequence of observations can now be formulated with the
sequence-to-sequence (seq2seq) framework which employs the chain rule to
efficiently rep... | [] | null | 55 | 1511.06391 | iclr_archive | [
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High-Dimensional Continuous Control Using Generalized Advantage Estimation | https://arxiv.org/abs/1506.02438 | [
"John Schulman",
"Philipp Moritz",
"Sergey Levine",
"Michael Jordan",
"Pieter Abbeel"
] | null | null | Policy gradient methods are an appealing approach in reinforcement learning
because they directly optimize the cumulative reward and can straightforwardly
be used with nonlinear function approximators such as neural networks. The two
main challenges are the large number of samples typically required, and the
difficul... | [] | null | 56 | 1506.02438 | iclr_archive | [
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BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies | https://arxiv.org/abs/1511.06909 | [
"[code] Shihao Ji",
"Swaminathan Vishwanathan",
"Nadathur Satish",
"Michael Anderson",
"Pradeep Dubey"
] | null | null | We propose BlackOut, an approximation algorithm to efficiently train massive
recurrent neural network language models (RNNLMs) with million word
vocabularies. BlackOut is motivated by using a discriminative loss, and we
describe a new sampling strategy which significantly reduces computation while
improving stability... | [] | null | 57 | 1511.06909 | iclr_archive | [
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Deep Multi Scale Video Prediction Beyond Mean Square Error | https://arxiv.org/abs/1511.05440 | [
"Michael Mathieu",
"camille couprie",
"Yann Lecun"
] | null | null | Learning to predict future images from a video sequence involves the
construction of an internal representation that models the image evolution
accurately, and therefore, to some degree, its content and dynamics. This is
why pixel-space video prediction may be viewed as a promising avenue for
unsupervised feature lea... | [] | null | 58 | 1511.05440 | iclr_archive | [
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0.0144... |
Grid Long Short-Term Memory | https://arxiv.org/abs/1507.01526 | [
"Nal Kalchbrenner",
"Alex Graves",
"Ivo Danihelka"
] | null | null | This paper introduces Grid Long Short-Term Memory, a network of LSTM cells
arranged in a multidimensional grid that can be applied to vectors, sequences
or higher dimensional data such as images. The network differs from existing
deep LSTM architectures in that the cells are connected between network layers
as well a... | [] | null | 59 | 1507.01526 | iclr_archive | [
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0.0... |
Net2Net: Accelerating Learning via Knowledge Transfer | https://arxiv.org/abs/1511.05641 | [
"Tianqi Chen",
"Ian Goodfellow",
"Jon Shlens"
] | null | null | We introduce techniques for rapidly transferring the information stored in
one neural net into another neural net. The main purpose is to accelerate the
training of a significantly larger neural net. During real-world workflows, one
often trains very many different neural networks during the experimentation and
desig... | [] | null | 60 | 1511.05641 | iclr_archive | [
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0... |
Predicting distributions with Linearizing Belief Networks | https://arxiv.org/abs/1511.05622 | [
"Yann Dauphin",
"David Grangier"
] | null | null | Conditional belief networks introduce stochastic binary variables in neural
networks. Contrary to a classical neural network, a belief network can predict
more than the expected value of the output $Y$ given the input $X$. It can
predict a distribution of outputs $Y$ which is useful when an input can admit
multiple o... | [] | null | 61 | 1511.05622 | iclr_archive | [
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0.006275642663240433,
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) | https://arxiv.org/abs/1511.07289 | [
"Djork-Arné Clevert",
"Thomas Unterthiner",
"Sepp Hochreiter"
] | null | null | We introduce the "exponential linear unit" (ELU) which speeds up learning in
deep neural networks and leads to higher classification accuracies. Like
rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs
(PReLUs), ELUs alleviate the vanishing gradient problem via the identity for
positive values... | [] | null | 62 | 1511.07289 | iclr_archive | [
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Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning | https://arxiv.org/abs/1511.06342 | [
"Emilio Parisotto",
"Jimmy Ba",
"Ruslan Salakhutdinov"
] | null | null | The ability to act in multiple environments and transfer previous knowledge
to new situations can be considered a critical aspect of any intelligent agent.
Towards this goal, we define a novel method of multitask and transfer learning
that enables an autonomous agent to learn how to behave in multiple tasks
simultane... | [] | null | 63 | 1511.06342 | iclr_archive | [
-0.0147046884521842,
-0.03715483471751213,
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0.01730639487504959,
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-0.03... |
Segmental Recurrent Neural Networks | https://arxiv.org/abs/1511.06018 | [
"Lingpeng Kong",
"Chris Dyer",
"Noah Smith"
] | null | null | We introduce segmental recurrent neural networks (SRNNs) which define, given
an input sequence, a joint probability distribution over segmentations of the
input and labelings of the segments. Representations of the input segments
(i.e., contiguous subsequences of the input) are computed by encoding their
constituent ... | [] | null | 64 | 1511.06018 | iclr_archive | [
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-0... |
Deep Linear Discriminant Analysis | https://arxiv.org/abs/1511.04707 | [
"[code] Matthias Dorfer",
"Rainer Kelz",
"Gerhard Widmer"
] | null | null | We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns
linearly separable latent representations in an end-to-end fashion. Classic LDA
extracts features which preserve class separability and is used for
dimensionality reduction for many classification problems. The central idea of
this paper is to put ... | [] | null | 65 | 1511.04707 | iclr_archive | [
0.012208878062665462,
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0.055701784789562225,
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... |
Large-Scale Approximate Kernel Canonical Correlation Analysis | https://arxiv.org/abs/1511.04773 | [
"Weiran Wang",
"Karen Livescu"
] | null | null | Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view
representation learning technique with broad applicability in statistics and
machine learning. Although there is a closed-form solution for the KCCA
objective, it involves solving an $N\times N$ eigenvalue system where $N$ is
the training set size... | [] | null | 66 | 1511.04773 | iclr_archive | [
-0.02605637162923813,
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... |
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | https://arxiv.org/abs/1511.06434 | [
"Alec Radford",
"Luke Metz",
"Soumith Chintala"
] | null | null | In recent years, supervised learning with convolutional networks (CNNs) has
seen huge adoption in computer vision applications. Comparatively, unsupervised
learning with CNNs has received less attention. In this work we hope to help
bridge the gap between the success of CNNs for supervised learning and
unsupervised l... | [] | null | 67 | 1511.06434 | iclr_archive | [
0.018260760232806206,
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0.004973947536200285,
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0... |
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks | https://arxiv.org/abs/1511.06448 | [
"[code] Pouya Bashivan",
"Irina Rish",
"Mohammed Yeasin",
"Noel Codella"
] | null | null | One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channe... | [] | null | 68 | 1511.06448 | iclr_archive | [
0.004930460825562477,
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-0.07720334082841873... |
Digging Deep into the layers of CNNs: In Search of How CNNs Achieve View Invariance | https://arxiv.org/abs/1508.01983 | [
"Amr Bakry",
"Mohamed Elhoseiny",
"Tarek El-Gaaly",
"Ahmed Elgammal"
] | null | null | This paper is focused on studying the view-manifold structure in the feature
spaces implied by the different layers of Convolutional Neural Networks (CNN).
There are several questions that this paper aims to answer: Does the learned
CNN representation achieve viewpoint invariance? How does it achieve viewpoint
invari... | [] | null | 69 | 1508.01983 | iclr_archive | [
0.0021490012295544147,
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0... |
An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family | https://arxiv.org/abs/1511.05042 | [
"Alexandre De Brébisson",
"Pascal Vincent"
] | null | null | In a multi-class classification problem, it is standard to model the output
of a neural network as a categorical distribution conditioned on the inputs.
The output must therefore be positive and sum to one, which is traditionally
enforced by a softmax. This probabilistic mapping allows to use the maximum
likelihood p... | [] | null | 70 | 1511.05042 | iclr_archive | [
-0.03689706698060036,
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0.021915823221206665,
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0.02290390431880951,
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-0.0... |
Data-Dependent Path Normalization in Neural Networks | https://arxiv.org/abs/1511.06747 | [
"Behnam Neyshabur",
"Ryota Tomioka",
"Ruslan Salakhutdinov",
"Nathan Srebro"
] | null | null | We propose a unified framework for neural net normalization, regularization
and optimization, which includes Path-SGD and Batch-Normalization and
interpolates between them across two different dimensions. Through this
framework we investigate issue of invariance of the optimization, data
dependence and the connection... | [] | null | 71 | 1511.06747 | iclr_archive | [
-0.011797702871263027,
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Reasoning in Vector Space: An Exploratory Study of Question Answering | https://arxiv.org/abs/1511.06426 | [
"Moontae Lee",
"Xiaodong He",
"Wen-tau Yih",
"Jianfeng Gao",
"Li Deng",
"Paul Smolensky"
] | null | null | Question answering tasks have shown remarkable progress with distributed
vector representation. In this paper, we investigate the recently proposed
Facebook bAbI tasks which consist of twenty different categories of questions
that require complex reasoning. Because the previous work on bAbI are all
end-to-end models,... | [] | null | 72 | 1511.06426 | iclr_archive | [
0.00012240871728863567,
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0.01594206877052784,
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... |
Neural GPUs Learn Algorithms | https://arxiv.org/abs/1511.08228 | [
"[code] [video] Lukasz Kaiser",
"Ilya Sutskever"
] | null | null | Learning an algorithm from examples is a fundamental problem that has been
widely studied. Recently it has been addressed using neural networks, in
particular by Neural Turing Machines (NTMs). These are fully differentiable
computers that use backpropagation to learn their own programming. Despite
their appeal NTMs h... | [] | null | 73 | 1511.08228 | iclr_archive | [
-0.004478097427636385,
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0.006937025114893913,
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... |
ACDC: A Structured Efficient Linear Layer | https://arxiv.org/abs/1511.05946 | [
"Marcin Moczulski",
"Misha Denil",
"Jeremy Appleyard",
"Nando de Freitas"
] | null | null | The linear layer is one of the most pervasive modules in deep learning
representations. However, it requires $O(N^2)$ parameters and $O(N^2)$
operations. These costs can be prohibitive in mobile applications or prevent
scaling in many domains. Here, we introduce a deep, differentiable,
fully-connected neural network ... | [] | null | 74 | 1511.05946 | iclr_archive | [
-0.010542670264840126,
0.00514606200158596,
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-0.02366439439356327,
-0.054361745715141296,
0.00... |
Density Modeling of Images using a Generalized Normalization Transformation | https://arxiv.org/abs/1511.06281 | [
"Johannes Ballé",
"Valero Laparra",
"Eero Simoncelli"
] | null | null | We introduce a parametric nonlinear transformation that is well-suited for
Gaussianizing data from natural images. The data are linearly transformed, and
each component is then normalized by a pooled activity measure, computed by
exponentiating a weighted sum of rectified and exponentiated components and a
constant. ... | [] | null | 75 | 1511.06281 | iclr_archive | [
0.0010950511787086725,
-0.014534329995512962,
0.015041871927678585,
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-0.025475382804870605,
-0.0170915350317955,
-0.049862202256917953,
-... |
Adversarial Manipulation of Deep Representations | https://arxiv.org/abs/1511.05122 | [
"[code] Sara Sabour",
"Yanshuai Cao",
"Fartash Faghri",
"David Fleet"
] | null | null | We show that the representation of an image in a deep neural network (DNN)
can be manipulated to mimic those of other natural images, with only minor,
imperceptible perturbations to the original image. Previous methods for
generating adversarial images focused on image perturbations designed to
produce erroneous clas... | [] | null | 76 | 1511.05122 | iclr_archive | [
0.0061780232936143875,
-0.009691739454865456,
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-0.048674650490283966,
-0.016330348327755928,
-0.05250536650419235,
... |
Geodesics of learned representations | https://arxiv.org/abs/1511.06394 | [
"Olivier Hénaff",
"Eero Simoncelli"
] | null | null | We develop a new method for visualizing and refining the invariances of
learned representations. Specifically, we test for a general form of
invariance, linearization, in which the action of a transformation is confined
to a low-dimensional subspace. Given two reference images (typically, differing
by some transforma... | [] | null | 77 | 1511.06394 | iclr_archive | [
-0.027192987501621246,
0.012899807654321194,
0.009373641572892666,
0.02263614349067211,
0.031909335404634476,
0.022706352174282074,
0.022894687950611115,
0.035677265375852585,
-0.05773557722568512,
-0.03911840543150902,
-0.01794447936117649,
-0.04486401006579399,
-0.05990774556994438,
0.01... |
Sequence Level Training with Recurrent Neural Networks | https://arxiv.org/abs/1511.06732 | [
"Marc'Aurelio Ranzato",
"Sumit Chopra",
"Michael Auli",
"Wojciech Zaremba"
] | null | null | Many natural language processing applications use language models to generate
text. These models are typically trained to predict the next word in a
sequence, given the previous words and some context such as an image. However,
at test time the model is expected to generate the entire sequence from
scratch. This disc... | [] | null | 78 | 1511.06732 | iclr_archive | [
-0.010025429539382458,
-0.019796235486865044,
-0.02642587013542652,
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-0.00425568874925375,
-0.015749596059322357,
0.03939025104045868,
-0.04685862362384796,
-0.006... |
Super-resolution with deep convolutional sufficient statistics | https://arxiv.org/abs/1511.05666 | [
"Joan Bruna",
"Pablo Sprechmann",
"Yann Lecun"
] | null | null | Inverse problems in image and audio, and super-resolution in particular, can
be seen as high-dimensional structured prediction problems, where the goal is
to characterize the conditional distribution of a high-resolution output given
its low-resolution corrupted observation. When the scaling ratio is small,
point est... | [] | null | 79 | 1511.05666 | iclr_archive | [
-0.012306218035519123,
-0.01619678921997547,
-0.0041568991728127,
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-0.07003657519817352,
-0.00461170356720686,
0.018833624199032784,
-0.05384916812181473,
0.02... |
Variational Gaussian Process | https://arxiv.org/abs/1511.06499 | [
"Dustin Tran",
"Rajesh Ranganath",
"David Blei"
] | null | null | Variational inference is a powerful tool for approximate inference, and it
has been recently applied for representation learning with deep generative
models. We develop the variational Gaussian process (VGP), a Bayesian
nonparametric variational family, which adapts its shape to match complex
posterior distributions.... | [] | null | 80 | 1511.06499 | iclr_archive | [
0.016261683776974678,
0.016947491094470024,
0.008608393371105194,
0.0468999519944191,
0.02068485878407955,
0.05858607962727547,
0.023597879335284233,
0.01110734511166811,
-0.01577882468700409,
-0.048966918140649796,
-0.03332630917429924,
0.005196025129407644,
-0.06839782744646072,
0.024543... |
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