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pro vyhledávání: '"Asokan, Siddarth"'
Autor:
Paliwal, Bhawna, Saini, Deepak, Dhawan, Mudit, Asokan, Siddarth, Natarajan, Nagarajan, Aggarwal, Surbhi, Malhotra, Pankaj, Jiao, Jian, Varma, Manik
Ranking a set of items based on their relevance to a given query is a core problem in search and recommendation. Transformer-based ranking models are the state-of-the-art approaches for such tasks, but they score each query-item independently, ignori
Externí odkaz:
http://arxiv.org/abs/2409.09795
Generative adversarial networks (GANs) comprise a generator, trained to learn the underlying distribution of the desired data, and a discriminator, trained to distinguish real samples from those output by the generator. A majority of GAN literature f
Externí odkaz:
http://arxiv.org/abs/2306.01654
We consider the problem of optimizing the discriminator in generative adversarial networks (GANs) subject to higher-order gradient regularization. We show analytically, via the least-squares (LSGAN) and Wasserstein (WGAN) GAN variants, that the discr
Externí odkaz:
http://arxiv.org/abs/2306.00785
Training Generative adversarial networks (GANs) stably is a challenging task. The generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data such as images. In this paper, we propose a novel approach for training G
Externí odkaz:
http://arxiv.org/abs/2305.07613
Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution. Variants such as conditional GANs, auxiliary-classifier GANs (ACGANs) project GANs on to supervised and se
Externí odkaz:
http://arxiv.org/abs/2010.15639
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