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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Regularized Learning for Domain Adaptation under Label Shifts | https://openreview.net/forum?id=rJl0r3R9KX | [
"Kamyar Azizzadenesheli",
"Anqi Liu",
"Fanny Yang",
"Animashree Anandkumar"
] | Poster | null | We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain. We first estimate importance weights using labeled source data and unlabeled target data, and then train a classifier ... | [
"Deep Learning",
"Domain Adaptation",
"Label Shift",
"Importance Weights",
"Generalization"
] | A practical and provably guaranteed approach for training efficiently classifiers in the presence of label shifts between Source and Target data sets | 1,592 | 1903.09734 | title_snapshot | [
-0.008355793543159962,
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Towards Robust, Locally Linear Deep Networks | https://openreview.net/forum?id=SylCrnCcFX | [
"Guang-He Lee",
"David Alvarez-Melis",
"Tommi S. Jaakkola"
] | Poster | null | Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction. One key challenge is tha... | [
"robust derivatives",
"transparency",
"interpretability"
] | A scalable algorithm to establish robust derivatives of deep networks w.r.t. the inputs. | 1,591 | 1907.03207 | title_snapshot | [
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... |
The Limitations of Adversarial Training and the Blind-Spot Attack | https://openreview.net/forum?id=HylTBhA5tQ | [
"Huan Zhang*",
"Hongge Chen*",
"Zhao Song",
"Duane Boning",
"Inderjit S. Dhillon",
"Cho-Jui Hsieh"
] | Poster | null | The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural net- works (DNNs). In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on... | [
"Adversarial Examples",
"Adversarial Training",
"Blind-Spot Attack"
] | We show that even the strongest adversarial training methods cannot defend against adversarial examples crafted on slightly scaled and shifted test images. | 1,584 | 1901.04684 | title_snapshot | [
-0.010875895619392395,
-0.036266982555389404,
-0.012159335426986217,
0.05345934256911278,
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0.0027403458952903748,
0.058111000806093216,
-0.003131832228973508,
-0.025561748072504997,
-0.041391726583242416,
-0.015448399819433689,
-0.0009182202047668397,
-0.0699156671762466... |
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference | https://openreview.net/forum?id=B1gTShAct7 | [
"Matthew Riemer",
"Ignacio Cases",
"Robert Ajemian",
"Miao Liu",
"Irina Rish",
"Yuhai Tu",
"and Gerald Tesauro"
] | Poster | null | Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off ... | [] | null | 1,583 | 1810.11910 | title_snapshot | [
-0.024284720420837402,
-0.01348612830042839,
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0... |
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue | https://openreview.net/forum?id=ryxnHhRqFm | [
"Chien-Sheng Wu",
"Richard Socher",
"Caiming Xiong"
] | Poster | null | End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share... | [
"pointer networks",
"memory networks",
"task-oriented dialogue systems",
"natural language processing"
] | GLMP: Global memory encoder (context RNN, global pointer) and local memory decoder (sketch RNN, local pointer) that share external knowledge (MemNN) are proposed to strengthen response generation in task-oriented dialogue. | 1,581 | 1901.04713 | title_snapshot | [
-0.02044672518968582,
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-0.0025801556184887886,
0.057665012776851654,
0.02694346196949482,
0.021194027736783028,
0.014421127736568451,
0.008368057198822498,
-0.00619416031986475,
-0.01939457096159458,
-0.028734708204865456,
0.025966906920075417,
-0.07483135908842087,
... |
Rethinking the Value of Network Pruning | https://openreview.net/forum?id=rJlnB3C5Ym | [
"Zhuang Liu",
"Mingjie Sun",
"Tinghui Zhou",
"Gao Huang",
"Trevor Darrell"
] | Poster | null | Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weigh... | [
"network pruning",
"network compression",
"architecture search",
"train from scratch"
] | In structured network pruning, fine-tuning a pruned model only gives comparable performance with training it from scratch. | 1,580 | 1810.05270 | title_snapshot | [
-0.008405479602515697,
-0.04458184912800789,
-0.014661682769656181,
0.04762870818376541,
0.038951609283685684,
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Neural TTS Stylization with Adversarial and Collaborative Games | https://openreview.net/forum?id=ByzcS3AcYX | [
"Shuang Ma",
"Daniel Mcduff",
"Yale Song"
] | Poster | null | The modeling of style when synthesizing natural human speech from text has been the focus of significant attention. Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud. The synthesized audio waveform is expected... | [
"Text-To-Speech synthesis",
"GANs"
] | a generative adversarial network for style modeling in a text-to-speech system | 1,570 | null | null | [
-0.0017406573751941323,
-0.0318998247385025,
-0.03508485481142998,
0.06421258300542831,
0.021945279091596603,
0.0426006019115448,
0.025180688127875328,
0.018695399165153503,
-0.014139171689748764,
-0.06229449808597565,
-0.056928981095552444,
0.028436193242669106,
-0.0520663782954216,
-0.00... |
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks | https://openreview.net/forum?id=SJe9rh0cFX | [
"Yukun Ding",
"Jinglan Liu",
"Jinjun Xiong",
"Yiyu Shi"
] | Poster | null | Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power... | [
"Quantized Neural Networks",
"Universial Approximability",
"Complexity Bounds",
"Optimal Bit-width"
] | This paper proves the universal approximability of quantized ReLU neural networks and puts forward the complexity bound given arbitrary error. | 1,567 | 1802.03646 | title_snapshot | [
-0.044808559119701385,
-0.01892954483628273,
-0.011225943453609943,
0.024501020088791847,
0.04610015079379082,
0.052417658269405365,
0.010766507126390934,
-0.011247378773987293,
-0.029387271031737328,
-0.013200259767472744,
-0.007714814972132444,
-0.00613827258348465,
-0.07839066535234451,
... |
Poincare Glove: Hyperbolic Word Embeddings | https://openreview.net/forum?id=Ske5r3AqK7 | [
"Alexandru Tifrea*",
"Gary Becigneul*",
"Octavian-Eugen Ganea*"
] | Poster | null | Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian... | [
"word embeddings",
"hyperbolic spaces",
"poincare ball",
"hypernymy",
"analogy",
"similarity",
"gaussian embeddings"
] | We embed words in the hyperbolic space and make the connection with the Gaussian word embeddings. | 1,565 | 1810.06546 | title_snapshot | [
-0.013699128292500973,
-0.019180674105882645,
0.010924606584012508,
0.04438673332333565,
0.034429483115673065,
0.04218379780650139,
0.03361944481730461,
-0.00006726705760229379,
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-0.05240834131836891,
0.002227034419775009,
0.007460111752152443,
-0.07335811853408813,
0... |
Eidetic 3D LSTM: A Model for Video Prediction and Beyond | https://openreview.net/forum?id=B1lKS2AqtX | [
"Yunbo Wang",
"Lu Jiang",
"Ming-Hsuan Yang",
"Li-Jia Li",
"Mingsheng Long",
"Li Fei-Fei"
] | Poster | null | Spatiotemporal predictive learning, though long considered to be a promising self-supervised feature learning method, seldom shows its effectiveness beyond future video prediction. The reason is that it is difficult to learn good representations for both short-term frame dependency and long-term high-level relations. W... | [] | null | 1,564 | null | null | [
0.015623487532138824,
-0.007794868666678667,
0.0034593725576996803,
0.027533922344446182,
0.04429279640316963,
0.02670375630259514,
0.02441786229610443,
0.01813993975520134,
-0.02736961841583252,
-0.026360347867012024,
0.011567243374884129,
-0.021583551540970802,
-0.042684201151132584,
0.0... |
Towards GAN Benchmarks Which Require Generalization | https://openreview.net/forum?id=HkxKH2AcFm | [
"Ishaan Gulrajani",
"Colin Raffel",
"Luke Metz"
] | Poster | null | For many evaluation metrics commonly used as benchmarks for unconditional image generation, trivially memorizing the training set attains a better score than models which are considered state-of-the-art; we consider this problematic.
We clarify a necessary condition for an evaluation metric not to behave this way: esti... | [
"evaluation",
"generative adversarial networks",
"adversarial divergences"
] | We argue that GAN benchmarks must require a large sample from the model to penalize memorization and investigate whether neural network divergences have this property. | 1,563 | 2001.03653 | title_snapshot | [
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0.014914372004568577,
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-0.04450530186295509,
0.001403281930834055,
0.025264669209718704,
-0.04905674234032631,
... |
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