Example: bankruptcy
Generative Adversarial Text to Image Synthesis
token conditioned on the image and all previous tokens, which is a more well-defined prediction problem. This conditional multi-modality is thus a very natural ap-plication for generative adversarial networks (Goodfellow et al.,2014), in which the generator network is optimized to fool the adversarially-trained discriminator into predicting
Tags:
Information
Domain:
Source:
Link to this page:
Documents from same domain
Show, Attend and Tell: Neural Image CaptionGeneration …
proceedings.mlr.pressShow, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu? KELVIN.XU@UMONTREAL.CA Jimmy Lei Bay JIMMY@PSI.UTORONTO.CA Ryan Kirosy RKIROS@CS.TORONTO.EDU Kyunghyun Cho?
Self-Attention Generative Adversarial Networks
proceedings.mlr.pressSelf-Attention Generative Adversarial Networks Figure 1. The proposed SAGAN generates images by leveraging complementary features in distant portions of the image rather than local regions of fixed shape to generate consistent objects/scenarios. In each row, the first image shows five representative query locations with color coded dots.
On the di culty of training recurrent neural networks
proceedings.mlr.pressOn the di culty of training recurrent neural networks @Et+1 @xt+1 Et Et+1 Et 1 xt 1 xt +1 ut +11 u tu @Et @xt @Et1 @xt1 @ xt +2 @xt +1 @x +1 x @xt1 @xt1 @xt2 Figure 2. Unrolling recurrent neural networks in time by creating a copy of the model for each time step.
TPOT: A Tree-based Pipeline Optimization Tool for ...
proceedings.mlr.pressJMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine …
Ensembles for Time Series Forecasting
proceedings.mlr.pressEnsembles for Time Series Forecasting set of real world time series. Our results clearly indicate that this is a promising research direction. In Section2we provide a brief description of the tasks being tackled in this paper.
Wasserstein Generative Adversarial Networks
proceedings.mlr.pressWasserstein Generative Adversarial Networks Figure 1: These plots show ˆ(P ;P 0) as a function of when ˆis the EM distance (left plot) or the JS divergence (right plot).The EM plot is continuous and provides a usable gradient everywhere.
Deep Gaussian Processes
proceedings.mlr.pressrepresentational power of a Gaussian process in the same role is significantly greater than that of an RBM. For the GP the corresponding likelihood is over a continuous vari-able, but it is a nonlinear function of the inputs, p(yjx) = N yjf(x);˙2; where N j ;˙2 is a Gaussian density with mean and variance ˙2. In this case the likelihood is ...
Noise-contrastive estimation: A new estimation principle ...
proceedings.mlr.pressated noise y. The estimation principle thus relies on noise with which the data is contrasted, so that we will refer to the new method as “noise-contrastive estima-tion”. In Section 2, we formally define noise-contrastive es-timation, establish fundamental statistical properties, and make the connection to supervised learning ex-plicit.
Gender Shades: Intersectional Accuracy Disparities in ...
proceedings.mlr.press117 million Americans are included in law en-forcement face recognition networks. A year-long research investigation across 100 police de-partments revealed that African-American indi-viduals are more likely to be stopped by law enforcement and be subjected to face recogni-tion searches than individuals of other ethnici-ties (Garvie et al.,2016).
Variational Inference with Normalizing Flows
proceedings.mlr.pressprovement in performance (Mnih & Gregor,2014). There is also a large body of evidence that describes the detri-mental effect of limited posterior approximations.Turner & Sahani(2011) provide an exposition of two commonly experienced problems. The first is the widely-observed problem of under-estimation of the variance of the poste-