ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 8 132 | paper_url stringlengths 41 41 | authors listlengths 1 17 | type stringclasses 2
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values | abstract large_stringlengths 228 1.98k | keywords listlengths 0 11 | TL;DR large_stringlengths 18 281 ⌀ | submission_number int64 1 1.17k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data | https://openreview.net/forum?id=ryBnUWb0b | [
"William Falcon",
"Henning Schulzrinne"
] | Poster | null | In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a b... | [
"Recurrent Neural Networks",
"RNN",
"LSTM",
"Mobile Device",
"Sensors"
] | We used an LSTM to detect when a smartphone walks into a building. Then we predict the device's floor level using data from sensors aboard the smartphone. | 682 | 1710.11122 | title_snapshot | [
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Identifying Analogies Across Domains | https://openreview.net/forum?id=BkN_r2lR- | [
"Yedid Hoshen",
"Lior Wolf"
] | Poster | null | Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching... | [
"unsupervised mapping",
"cross domain mapping"
] | Finding correspondences between domains by performing matching/mapping iterations | 390 | null | null | [
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Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling | https://openreview.net/forum?id=H1cWzoxA- | [
"Tao Shen",
"Tianyi Zhou",
"Guodong Long",
"Jing Jiang",
"Chengqi Zhang"
] | Poster | null | Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some ta... | [
"deep learning",
"attention mechanism",
"sequence modeling",
"natural language processing",
"sentence embedding"
] | A self-attention network for RNN/CNN-free sequence encoding with small memory consumption, highly parallelizable computation and state-of-the-art performance on several NLP tasks | 366 | 1804.00857 | title_snapshot | [
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WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling | https://openreview.net/forum?id=S1cZsf-RW | [
"Hao Zhang",
"Bo Chen",
"Dandan Guo",
"Mingyuan Zhou"
] | Poster | null | To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and... | [] | null | 916 | 1803.01328 | title_snapshot | [
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Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models | https://openreview.net/forum?id=BkJ3ibb0- | [
"Pouya Samangouei",
"Maya Kabkab",
"Rama Chellappa"
] | Poster | null | In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new fra... | [] | Defense-GAN uses a Generative Adversarial Network to defend against white-box and black-box attacks in classification models. | 714 | 1805.06605 | title_snapshot | [
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Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration | https://openreview.net/forum?id=S1DWPP1A- | [
"Alexandre Péré",
"Sébastien Forestier",
"Olivier Sigaud",
"Pierre-Yves Oudeyer"
] | Poster | null | Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action ... | [
"exploration; autonomous goal setting; diversity; unsupervised learning; deep neural network"
] | We propose a novel Intrinsically Motivated Goal Exploration architecture with unsupervised learning of goal space representations, and evaluate how various implementations enable the discovery of a diversity of policies. | 132 | 1803.00781 | title_snapshot | [
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Word translation without parallel data | https://openreview.net/forum?id=H196sainb | [
"Guillaume Lample",
"Alexis Conneau",
"Marc'Aurelio Ranzato",
"Ludovic Denoyer",
"Hervé Jégou"
] | Poster | null | State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with th... | [
"unsupervised learning",
"machine translation",
"multilingual embeddings",
"parallel dictionary induction",
"adversarial training"
] | Aligning languages without the Rosetta Stone: with no parallel data, we construct bilingual dictionaries using adversarial training, cross-domain local scaling, and an accurate proxy criterion for cross-validation. | 7 | 1710.04087 | title_snapshot | [
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Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties | https://openreview.net/forum?id=SysEexbRb | [
"Yi Zhou",
"Yingbin Liang"
] | Poster | null | Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect. Particularly, the properties of critical points and the landscape around them are of importance to determine ... | [
"neural networks",
"critical points",
"analytical form",
"landscape"
] | We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks. | 549 | 1710.11205 | title_judge | [
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Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm | https://openreview.net/forum?id=HyjC5yWCW | [
"Chelsea Finn",
"Sergey Levine"
] | Poster | null | Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternativel... | [
"meta-learning",
"learning to learn",
"universal function approximation"
] | Deep representations combined with gradient descent can approximate any learning algorithm. | 513 | 1710.11622 | title_snapshot | [
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Maximum a Posteriori Policy Optimisation | https://openreview.net/forum?id=S1ANxQW0b | [
"Abbas Abdolmaleki",
"Jost Tobias Springenberg",
"Yuval Tassa",
"Remi Munos",
"Nicolas Heess",
"Martin Riedmiller"
] | Poster | null | We introduce a new algorithm for reinforcement learning called Maximum a-posteriori Policy Optimisation (MPO) based on coordinate ascent on a relative-entropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are co... | [
"Reinforcement Learning",
"Variational Inference",
"Control"
] | null | 1,110 | 1806.06920 | title_snapshot | [
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The Implicit Bias of Gradient Descent on Separable Data | https://openreview.net/forum?id=r1q7n9gAb | [
"Daniel Soudry",
"Elad Hoffer",
"Mor Shpigel Nacson",
"Nathan Srebro"
] | Poster | null | We show that gradient descent on an unregularized logistic regression
problem, for almost all separable datasets, converges to the same direction as the max-margin solution. The result generalizes also to other monotone decreasing loss functions with an infimum at infinity, and we also discuss a multi-class generalizat... | [
"gradient descent",
"implicit regularization",
"generalization",
"margin",
"logistic regression",
"loss functions",
"optimization",
"exponential tail",
"cross-entropy"
] | The normalized solution of gradient descent on logistic regression (or a similarly decaying loss) slowly converges to the L2 max margin solution on separable data. | 358 | 1710.10345 | title_snapshot | [
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