ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
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... |
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