Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples. The problems essentially arise from the two-player formulation...

This paper presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA. Similar to previous works on adversarial learning for dialogue generation, our method assumes the GCA as a generator that aims at fooling a discriminator that labels dialogues as human-generated or machine-generated; however, in our approach, the discriminator performs to...

For many tRNAs, the discriminator base preceding the CCA sequence at the 3' end is important for aminoacylation. We show that the discriminator base influences the stability of the 1.72 base pair onto which it is stacked. Mutations of the discriminator base from adenosine to cytidine or uridine make the cytidine residue in the C1-G72 base pair of mutant Escherichia coli initiator tRNAs more rea...

Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a d...

BACKGROUND Respiratory complications remain a major cause of mortality among individuals with spinal-cord injury (SCI). The present study investigated whether respiratory function is a discriminator of pneumonia in individuals with SCI and is aimed to determine the best predictive parameter. METHODS This was a retrospective cohort study. Individuals with traumatic SCI, level C3 to T12, comple...

In this paper, we propose an all-digital frequency output of the synthesizer. High reference frequency (REF) is synthesizer architecture, based on an all-digital XA-frequency used to push the spurs far from the carrier. The need for high discriminator. The new all-digital synthesizer is compared to sampling frequency, in addition to the resolution being previously published work. The architectu...

We introduce an adversarial learning framework, which we named KBGAN, to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graph datasets typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventi...

OBJECTIVE To discriminate seizures from interictal dynamics based on multivariate synchrony measures, and to identify dynamics of a pre-seizure state. METHODS A linear discriminator was constructed from two different measures of synchronization: cross-correlation and phase synchronization. We applied this discriminator to a sequence of seizures recorded from the intracranial EEG of a patient ...

Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background sta...

We propose to extend the original generative adversarial networks (GANs) framework to multiple discriminators and omit, or dropout, the feedback of each discriminator with same probability at the end of each batch. Our approach forces the generator to not rely on a given discriminator to learn how to produce realistic looking samples, but, instead, on a dynamic ensemble of adversaries. This pro...