ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 14 131 | paper_url stringlengths 42 42 | authors listlengths 1 15 | type stringclasses 3
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values | abstract large_stringlengths 332 2.13k | keywords listlengths 0 21 | TL;DR large_stringlengths 31 250 ⌀ | submission_number int64 1 2.59k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Pay Attention to Features, Transfer Learn Faster CNNs | https://openreview.net/forum?id=ryxyCeHtPB | [
"Kafeng Wang",
"Xitong Gao",
"Yiren Zhao",
"Xingjian Li",
"Dejing Dou",
"Cheng-Zhong Xu"
] | Poster | null | Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance. Transfer learning offers the chance for CNNs to learn with limited data samples by transferring knowledge from models pretrained on large datasets. Blindly transfer... | [
"transfer learning",
"pruning",
"faster CNNs"
] | We introduce attentive feature distillation and selection, to fine-tune a large model and produce a faster one. | 2,594 | null | null | [
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Geom-GCN: Geometric Graph Convolutional Networks | https://openreview.net/forum?id=S1e2agrFvS | [
"Hongbin Pei",
"Bingzhe Wei",
"Kevin Chen-Chuan Chang",
"Yu Lei",
"Bo Yang"
] | Spotlight | null | Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the abilit... | [
"Deep Learning",
"Graph Convolutional Network",
"Network Geometry"
] | For graph neural networks, the aggregation on a graph can benefit from a continuous space underlying the graph. | 2,589 | 2002.05287 | title_snapshot | [
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Gradients as Features for Deep Representation Learning | https://openreview.net/forum?id=BkeoaeHKDS | [
"Fangzhou Mu",
"Yingyu Liang",
"Yin Li"
] | Poster | null | We address the challenging problem of deep representation learning -- the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our... | [
"representation learning",
"gradient features",
"deep learning"
] | Given a pre-trained model, we explored the per-sample gradients of the model parameters relative to a task-specific loss, and constructed a linear model that combines gradients of model parameters and the activation of the model. | 2,585 | 2004.05529 | title_snapshot | [
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Monotonic Multihead Attention | https://openreview.net/forum?id=Hyg96gBKPS | [
"Xutai Ma",
"Juan Miguel Pino",
"James Cross",
"Liezl Puzon",
"Jiatao Gu"
] | Poster | null | Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approach for this task either apply a fixed policy on transformer, or a learnable monotonic attention on a weaker recurrent neural network based structure. In this paper, we propose a ... | [
"Simultaneous Translation",
"Transformer",
"Monotonic Attention"
] | Make the transformer streamable with monotonic attention. | 2,583 | 1909.12406 | title_snapshot | [
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Massively Multilingual Sparse Word Representations | https://openreview.net/forum?id=HyeYTgrFPB | [
"Gábor Berend"
] | Poster | null | In this paper, we introduce Mamus for constructing multilingual sparse word representations. Our algorithm operates by determining a shared set of semantic units which get reutilized across languages, providing it a competitive edge both in terms of speed and evaluation performance. We demonstrate that our proposed alg... | [
"sparse word representations",
"multilinguality",
"sparse coding"
] | We propose an efficient algorithm for determining multilingually comparable sparse word representations that we release for 27 typologically diverse languages. | 2,582 | null | null | [
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Query-efficient Meta Attack to Deep Neural Networks | https://openreview.net/forum?id=Skxd6gSYDS | [
"Jiawei Du",
"Hu Zhang",
"Joey Tianyi Zhou",
"Yi Yang",
"Jiashi Feng"
] | Poster | null | Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtainin... | [
"Adversarial attack",
"Meta learning"
] | null | 2,580 | 1906.02398 | title_snapshot | [
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BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES | https://openreview.net/forum?id=HJxdTxHYvB | [
"Amin Ghiasi",
"Ali Shafahi",
"Tom Goldstein"
] | Poster | null | Defenses against adversarial attacks can be classified into certified and non-certified. Certifiable defenses make networks robust within a certain $\ell_p$-bounded radius, so that it is impossible for the adversary to make adversarial examples in the certificate bound. We present an attack that maintains the impercept... | [] | null | 2,579 | 2003.08937 | title_snapshot | [
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An Exponential Learning Rate Schedule for Deep Learning | https://openreview.net/forum?id=rJg8TeSFDH | [
"Zhiyuan Li",
"Sanjeev Arora"
] | Spotlight | null | Intriguing empirical evidence exists that deep learning can work well with exotic schedules for varying the learning rate. This paper suggests that the phenomenon may be due to Batch Normalization or BN(Ioffe & Szegedy, 2015), which is ubiq- uitous and provides benefits in optimization and generalization across all sta... | [
"batch normalization",
"weight decay",
"learning rate",
"deep learning theory"
] | We propose an exponentially growing learning rate schedule for networks with BatchNorm, which surprisingly performs well in practice and is provably equivalent to popular LR schedules like Step Decay. | 2,575 | 1910.07454 | title_snapshot | [
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Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation | https://openreview.net/forum?id=B1xSperKvH | [
"Nitin Rathi",
"Gopalakrishnan Srinivasan",
"Priyadarshini Panda",
"Kaushik Roy"
] | Poster | null | Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and se... | [
"spiking neural networks",
"ann-snn conversion",
"spike-based backpropagation",
"imagenet"
] | A hybrid training technique that combines ANN-SNN conversion and spike-based backpropagation to optimize training effort and inference latency. | 2,573 | 2005.01807 | title_snapshot | [
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... |
How to 0wn the NAS in Your Spare Time | https://openreview.net/forum?id=S1erpeBFPB | [
"Sanghyun Hong",
"Michael Davinroy",
"Yiǧitcan Kaya",
"Dana Dachman-Soled",
"Tudor Dumitraş"
] | Poster | null | New data processing pipelines and novel network architectures increasingly drive the success of deep learning. In consequence, the industry considers top-performing architectures as intellectual property and devotes considerable computational resources to discovering such architectures through neural architecture searc... | [
"Reconstructing Novel Deep Learning Systems"
] | We design an algorithm that reconstructs the key components of a novel deep learning system by exploiting a small amount of information leakage from a cache side-channel attack, Flush+Reload. | 2,572 | 2002.06776 | title_judge | [
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The Shape of Data: Intrinsic Distance for Data Distributions | https://openreview.net/forum?id=HyebplHYwB | [
"Anton Tsitsulin",
"Marina Munkhoeva",
"Davide Mottin",
"Panagiotis Karras",
"Alex Bronstein",
"Ivan Oseledets",
"Emmanuel Mueller"
] | Poster | null | The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures. Existing techniques for comparing data distributions focus on global data properties such as mean and covariance; in that se... | [
"Deep Learning",
"Generative Models",
"Nonlinear Dimensionality Reduction",
"Manifold Learning",
"Similarity and Distance Learning",
"Spectral Methods"
] | We propose a metric for comparing data distributions based on their geometry while not relying on any positional information. | 2,564 | 1905.11141 | title_snapshot | [
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-0.0176684707403183,
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0.02... |
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