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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
FractalNet: Ultra-Deep Neural Networks without Residuals | https://openreview.net/forum?id=S1VaB4cex | [
"Gustav Larsson",
"Michael Maire",
"Gregory Shakhnarovich"
] | Poster | null | We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pas... | [] | 214 | 1605.07648 | title_snapshot | [
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Deep Information Propagation | https://openreview.net/forum?id=H1W1UN9gg | [
"Samuel S. Schoenholz",
"Justin Gilmer",
"Surya Ganguli",
"Jascha Sohl-Dickstein"
] | Poster | null | We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory. We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be... | [
"Theory",
"Deep learning"
] | We predict whether randomly initialized neural networks can be trained by studying whether or not information can travel through them. | 215 | 1611.01232 | title_snapshot | [
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Pruning Convolutional Neural Networks for Resource Efficient Inference | https://openreview.net/forum?id=SJGCiw5gl | [
"Pavlo Molchanov",
"Stephen Tyree",
"Tero Karras",
"Timo Aila",
"Jan Kautz"
] | Poster | null | We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation-a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion base... | [
"Deep learning",
"Transfer Learning"
] | New approach for removing unnecessary conv neurons from network. Work is focused on how to estimate importance fast and efficiently by Taylor expantion. | 427 | 1611.06440 | title_snapshot | [
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... |
Recurrent Batch Normalization | https://openreview.net/forum?id=r1VdcHcxx | [
"Tim Cooijmans",
"Nicolas Ballas",
"César Laurent",
"Çağlar Gülçehre",
"Aaron Courville"
] | Poster | null | We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transi... | [
"Deep learning",
"Optimization"
] | Make batch normalization work in recurrent neural networks | 264 | 1603.09025 | title_snapshot | [
-0.008008279837667942,
-0.0391501821577549,
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0.03159122169017792,
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework | https://openreview.net/forum?id=Sy2fzU9gl | [
"Irina Higgins",
"Loic Matthey",
"Arka Pal",
"Christopher Burgess",
"Xavier Glorot",
"Matthew Botvinick",
"Shakir Mohamed",
"Alexander Lerchner"
] | Poster | null | Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state-of-the-art framewor... | [] | We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. | 291 | null | null | [
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0.04098096862435341,
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Words or Characters? Fine-grained Gating for Reading Comprehension | https://openreview.net/forum?id=B1hdzd5lg | [
"Zhilin Yang",
"Bhuwan Dhingra",
"Ye Yuan",
"Junjie Hu",
"William W. Cohen",
"Ruslan Salakhutdinov"
] | Poster | null | Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the ... | [
"Natural language processing",
"Deep learning"
] | 453 | 1611.01724 | title_snapshot | [
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DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning | https://openreview.net/forum?id=Bks8cPcxe | [
"Tian Zhao",
"Xiao Bing Huang",
"Yu Cao"
] | Poster | null | In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the-art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domai... | [
"Deep learning",
"Applications",
"Optimization"
] | DeepDSL(a DSL embedded in Scala) that compiles deep learning networks written in DeepDSL to Java source code, which runs on any GPU equipped machines with competitive efficiency as existing state-of-the-art tools (e.g. Caffe and Tensorflow) | 414 | 1701.02284 | title_snapshot | [
-0.0395638644695282,
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0.022452116012573242,
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0.025... |
HyperNetworks | https://openreview.net/forum?id=rkpACe1lx | [
"David Ha",
"Andrew M. Dai",
"Quoc V. Le"
] | Poster | null | This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our ... | [
"Natural language processing",
"Deep learning",
"Supervised Learning"
] | We train a small RNN to generate weights for a larger RNN, and train the system end-to-end. We obtain state-of-the-art results on a variety of sequence modelling tasks. | 8 | 1609.09106 | title_snapshot | [
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0.04339401796460152,
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0.00... |
Capacity and Trainability in Recurrent Neural Networks | https://openreview.net/forum?id=BydARw9ex | [
"Jasmine Collins",
"Jascha Sohl-Dickstein",
"David Sussillo"
] | Poster | null | Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and pe... | [
"Deep learning"
] | 447 | 1611.09913 | title_snapshot | [
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0.005810367409139872,
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-0.05831291154026985,
0.0... | |
Recurrent Hidden Semi-Markov Model | https://openreview.net/forum?id=HJGODLqgx | [
"Hanjun Dai",
"Bo Dai",
"Yan-Ming Zhang",
"Shuang Li",
"Le Song"
] | Poster | null | Segmentation and labeling of high dimensional time series data has wide applications in behavior understanding and medical diagnosis. Due to the difficulty in obtaining the label information for high dimensional data, realizing this objective in an unsupervised way is highly desirable. Hidden Semi-Markov Model (HSMM) i... | [
"Deep learning",
"Unsupervised Learning",
"Structured prediction"
] | We propose to incorporate the RNN to model the generative process in Hidden Semi-Markov Model for unsupervised segmentation and labeling. | 300 | null | null | [
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-0.035369258373975754,
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-0.032212890684604645,
0... |
Learning Curve Prediction with Bayesian Neural Networks | https://openreview.net/forum?id=S11KBYclx | [
"Aaron Klein",
"Stefan Falkner",
"Jost Tobias Springenberg",
"Frank Hutter"
] | Poster | null | Different neural network architectures, hyperparameters and training protocols lead to different performances as a function of time.
Human experts routinely inspect the resulting learning curves to quickly terminate runs with poor hyperparameter settings and thereby considerably speed up manual hyperparameter optimizat... | [
"Deep learning",
"Applications"
] | We present a general probabilistic method based on Bayesian neural networks to predit learning curves of iterative machine learning methods. | 488 | null | null | [
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0.0284996647387743,
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... |
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