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Distilling the Knowledge from Conditional Normalizing Flows [article]

Dmitry Baranchuk, Vladimir Aliev, Artem Babenko
2021 arXiv   pre-print

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2021 pre-print
arXiv:2106.12699v1 fatcat:wiadifeuarek7hgfujbsvstldi
We provide a positive answer to this question by proposing a simple distillation approach and demonstrating its effectiveness on state-of-the-art conditional flow-based models for image super-resolution  ...  In this work, we investigate whether one can distill flow-based models into more efficient alternatives.  ...  .), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp. 2078-2087, Stockholmsmässan, Stockholm Sweden, 10-15 Jul 2018.  ... 
arXiv:2106.12699v3 fatcat:7fn3hnwtyrbqbhdvkozp2rprfm
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Dmitry Baranchuk, Vladimir Aliev, Artem Babenko. "Distilling the Knowledge from Conditional Normalizing Flows." arXiv (2021)

Hyperbolic Graph Neural Networks [article]

Qi Liu, Maximilian Nickel, Douwe Kiela
2019 arXiv   pre-print

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We develop a scalable algorithm for modeling the structural properties of graphs, comparing Euclidean and hyperbolic geometry.  ...  Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise.  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 2328–2337, 2018. [26] Thomas N.  ... 
arXiv:1910.12892v1 fatcat:zpc5bwjlyrbbxlwvnflxr4jv4y
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Qi Liu, Maximilian Nickel, Douwe Kiela. "Hyperbolic Graph Neural Networks." arXiv (2019)

KNAS: Green Neural Architecture Search [article]

Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, Hongxia Yang
2021 arXiv   pre-print

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Experiments show that KNAS achieves competitive results with orders of magnitude faster than "train-then-test" paradigms on image classification tasks.  ...  Furthermore, the extremely low search cost enables its wide applications. The searched network also outperforms strong baseline RoBERTA-large on two text classification tasks. Codes are available at .  ...  In Proceedings of the 36th International Conference on Machine Learning, pp. 4095– International Conference on Machine Learning, ICML 4104.  ... 
arXiv:2111.13293v1 fatcat:fz4p37bk2jgxnn2qfc6ffvvfwu
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Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu Sun, Hongxia Yang. "KNAS: Green Neural Architecture Search." arXiv (2021)

Variational Neural Machine Translation with Normalizing Flows [article]

Hendra Setiawan, Matthias Sperber, Udhay Nallasamy, Matthias Paulik
2020 arXiv   pre-print

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Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables  ...  Previous works impose strong assumptions on the distribution of the latent code and limit the choice of the NMT architecture.  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 159-168.  ... 
arXiv:2005.13978v1 fatcat:7ysqtt4kgzgm3l4kddct7byihy
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Hendra Setiawan, Matthias Sperber, Udhay Nallasamy, Matthias Paulik. "Variational Neural Machine Translation with Normalizing Flows." arXiv (2020)

Learning Compressed Transforms with Low Displacement Rank [article]

Anna T. Thomas and Albert Gu and Tri Dao and Atri Rudra and Christopher Ré
2019 arXiv   pre-print

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2018 pre-print
arXiv:1810.02309v2 fatcat:f3if7cpcmjetvn62vfdgms2cji
We prove bounds on the VC dimension of multi-layer neural networks with structured weight matrices and show empirically that our compact parameterization can reduce the sample complexity of learning.  ...  We introduce a class of LDR matrices with more general displacement operators, and explicitly learn over both the operators and the low-rank component.  ...  , of DARPA, NIH, ONR, or the U.S.  ... 
arXiv:1810.02309v3 fatcat:hf2tf74hn5hojfmhzo54gt6fhm
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Anna T. Thomas and Albert Gu and Tri Dao and Atri Rudra and Christopher Ré. "Learning Compressed Transforms with Low Displacement Rank." arXiv (2019)

PARL: Enhancing Diversity of Ensemble Networks to Resist Adversarial Attacks via Pairwise Adversarially Robust Loss Function [article]

Manaar Alam, Shubhajit Datta, Debdeep Mukhopadhyay, Arijit Mondal, Partha Pratim Chakrabarti
2021 arXiv   pre-print

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The security of Deep Learning classifiers is a critical field of study because of the existence of adversarial attacks.  ...  Such attacks usually rely on the principle of transferability, where an adversarial example crafted on a surrogate classifier tends to mislead the target classifier trained on the same dataset even if  ...  , Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 274–283.  ... 
arXiv:2112.04948v1 fatcat:i7ab4hvgprcgvdpnowrlwmaiwa
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Manaar Alam, Shubhajit Datta, Debdeep Mukhopadhyay, Arijit Mondal, Partha Pratim Chakrabarti. "PARL: Enhancing Diversity of Ensemble Networks to Resist Adversarial Attacks via Pairwise Adversarially Robust Loss Function." arXiv (2021)

Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics [article]

Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers
2021 arXiv   pre-print

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Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics.  ...  We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Ma- chine Learning Research, pages  ... 
arXiv:2110.01518v1 fatcat:orftxwh7uvcwdkpamabwqzxgti
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Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers. "Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics." arXiv (2021)

A Novel Sequential Coreset Method for Gradient Descent Algorithms [article]

Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos M. Freris, Hu Ding
2021 arXiv   pre-print

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A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset  ...  In this paper, based on the "locality" property of gradient descent algorithms, we propose a new framework, termed "sequential coreset", which effectively avoids these obstacles.  ...  Dy and Andreas Krause, ed- itors, Proceedings of the 35th International Conference on Machine Learn- ing, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of  ... 
arXiv:2112.02504v1 fatcat:ca6ik4vfgfgqrmoqottujcbhwa
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Jiawei Huang, Ruomin Huang, Wenjie Liu, Nikolaos M. Freris, Hu Ding. "A Novel Sequential Coreset Method for Gradient Descent Algorithms." arXiv (2021)

Deep Learning for Text Style Transfer: A Survey [article]

Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
2021 arXiv   pre-print

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2021 pre-print
arXiv:2011.00416v4 fatcat:d2yjzuhf6vfzfajkiqzie6dtm4
2021 pre-print
arXiv:2011.00416v3 fatcat:mfrdc3h6ejhrncn2snqqvxsf2u
2020 pre-print
arXiv:2011.00416v2 fatcat:br3itaeihzdkjha2ixnnxbotqa
2020 pre-print
arXiv:2011.00416v1 fatcat:p5qqzw4nuvcwrn74xsw776guya
We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_Survey  ...  In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017.  ...  Conference on Machine Learning, ICML 2018, Zhang, Jingyi, Masao Utiyama, Eiichiro Stockholmsmässan, Stockholm, Sweden, July Sumita, Graham Neubig, and Satoshi 10-15, 2018,  ... 
arXiv:2011.00416v5 fatcat:wfw3jfh2mjfupbzrmnztsqy4ny
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Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea. "Deep Learning for Text Style Transfer: A Survey." arXiv (2021)

Automated Discovery of Adaptive Attacks on Adversarial Defenses [article]

Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev
2021 arXiv   pre-print

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2021 pre-print
arXiv:2102.11860v1 fatcat:czx4wczm3zdpjey2jzqzorxomy
We evaluated our approach on 24 adversarial defenses and show that it outperforms AutoAttack, the current state-of-the-art tool for reliable evaluation of adversarial defenses: our tool discovered significantly  ...  Reliable evaluation of adversarial defenses is a challenging task, currently limited to an expert who manually crafts attacks that exploit the defense's inner workings or approaches based on an ensemble  ...  (eds.), Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pp  ... 
arXiv:2102.11860v3 fatcat:fewctvqk6zg4bagc4lvstpqrgq
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Chengyuan Yao, Pavol Bielik, Petar Tsankov, Martin Vechev. "Automated Discovery of Adaptive Attacks on Adversarial Defenses." arXiv (2021)

FNNC: Achieving Fairness through Neural Networks [article]

Padala Manisha, Sujit Gujar
2020 arXiv   pre-print

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2020 pre-print
arXiv:1811.00247v2 fatcat:ayaswg5if5anlo4gnm5dt5cvse
2018 pre-print
arXiv:1811.00247v1 fatcat:p7coo23fkrf47dgi3n2vj6oazm
The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better.  ...  The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero.  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 3381-3390, 2018.  ... 
arXiv:1811.00247v3 fatcat:rjtpzeo3nng4jgjpa7avklfod4
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Padala Manisha, Sujit Gujar. "FNNC: Achieving Fairness through Neural Networks." arXiv (2020)

Query complexity of adversarial attacks [article]

Grzegorz Głuch, Rüdiger Urbanke
2021 arXiv   pre-print

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arXiv:2010.01039v1 fatcat:snrdan6lxzazzilgf7uommld2q
We give a lower bound on that number of queries in terms of entropy of decision boundaries of the classifier.  ...  Using this result we analyze two classical learning algorithms on two synthetic tasks for which we prove meaningful security guarantees.  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pp. 274-283, 2018.  ... 
arXiv:2010.01039v2 fatcat:th5mrgldfnahfl2n3bp6uokybm
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Grzegorz Głuch, Rüdiger Urbanke. "Query complexity of adversarial attacks." arXiv (2021)

On the Effectiveness of Iterative Learning Control [article]

Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
2021 arXiv   pre-print

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arXiv:2111.09434v2 fatcat:w6e4cx4u2fbpjoxslblkzmlvje
2021 pre-print
arXiv:2111.09434v1 fatcat:46hmkbponjbwvfnap3yiiwapde
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications.  ...  Our work presents such a theoretical study of the performance of both ILC and MM on Linear Quadratic Regulator (LQR) problems with unknown transition dynamics.  ...  Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of  ... 
arXiv:2111.09434v3 fatcat:lwnqcrx4wneiddqrgwq2dpke4q
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Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell. "On the Effectiveness of Iterative Learning Control." arXiv (2021)

Deep Learning for Text Style Transfer: A Survey

Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
2021 Computational Linguistics  

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We also provide discussions on a variety of important topics regarding the future development of this task.  ...  In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017.  ...  Conference on Machine Learning, ICML 2018, Zhang, Jingyi, Masao Utiyama, Eiichiro Stockholmsmässan, Stockholm, Sweden, July Sumita, Graham Neubig, and Satoshi 10-15, 2018  ... 
doi:10.1162/coli_a_00426 fatcat:v7vmb62ckfcu5k5mpu2pydnrxy
DOAJ Szczepanski
Citation

Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea. "Deep Learning for Text Style Transfer: A Survey." Computational Linguistics (2021) 1-51

Regex Queries over Incomplete Knowledge Bases [article]

Vaibhav Adlakha, Parth Shah, Srikanta Bedathur, Mausam
2021 arXiv   pre-print

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2020 pre-print arXiv:2005.00480v1 fatcat:vd5fxdpjmfeblkq5yoniz75ve4
We demonstrate performance of RotatE-Box on two new regex-query datasets introduced in this paper, including one where the queries are harvested based on actual user query logs.  ...  We propose the novel task of answering regular expression queries (containing disjunction (∨) and Kleene plus (+) operators) over incomplete KBs.  ...  In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 2869-2878. Ni Lao, Tom Mitchell, and William W.  ... 
arXiv:2005.00480v2 fatcat:c3f6m437pvarzffdggvovzvmka
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Vaibhav Adlakha, Parth Shah, Srikanta Bedathur, Mausam. "Regex Queries over Incomplete Knowledge Bases." arXiv (2021)

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