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02/03/2023
Clustered Embedding Learning for Recommender Systems
In recent years, recommender systems have advanced rapidly, where embedd...
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12/07/2020
Sobolev Wasserstein GAN
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) dua...
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03/14/2020
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Recent advances in large-scale optimal transport have greatly extended i...
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11/21/2019
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source doma...
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05/25/2019
Quantifying Exposure Bias for Neural Language Generation
The exposure bias problem refers to the training-inference discrepancy c...
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05/25/2019
Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Knowledge base is one of the main forms to represent information in a st...
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04/02/2019
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Lipschitz continuity recently becomes popular in generative adversarial ...
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02/15/2019
Lipschitz Generative Adversarial Nets
In this paper we study the convergence of generative adversarial network...
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11/15/2018
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
CycleGAN is capable of learning a one-to-one mapping between two data di...
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09/29/2018
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Adam is shown not being able to converge to the optimal solution in cert...
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07/02/2018
Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis
This paper aims to bring a new perspective for understanding GANs, by de...
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10/17/2017
Face Transfer with Generative Adversarial Network
Face transfer animates the facial performances of the character in the t...
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08/05/2017
Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
In this paper, we study several GAN related topics mathematically, inclu...
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07/05/2017
Learning to Design Games: Strategic Environments in Deep Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given...
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03/06/2017
Activation Maximization Generative Adversarial Nets
Class label information has been empirically proven to be very useful in...
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02/22/2017