TPOT: A Tree-based Pipeline Optimization Tool for ...

JMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine …

  Automating, Machine, Tool, Pipeline, Optimization, Pipeline optimization tool for automating machine

Ensembles for Time Series Forecasting

Ensembles 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.

  Series, Time, Time series, Forecasting, Beslenme, Ensembles for time series forecasting

Show, Attend and Tell: Neural Image CaptionGeneration …

Show, 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?

  Image, Attention, Neural, Tell, And tell, Neural image captiongeneration, Captiongeneration

Wasserstein Generative Adversarial Networks

Wasserstein 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.

  Network, Adversarial, Generative, Wasserstein generative adversarial networks, Wasserstein

Self-Attention Generative Adversarial Networks

Self-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.

  Network, Self, Attention, Adversarial, Generative, Self attention generative adversarial networks

Generative Adversarial Text to Image Synthesis

deep convolutional decoder networks to generate realistic images.Dosovitskiy et al.(2015) trained a deconvolutional network (several layers of convolution and upsampling) to generate 3D chair renderings conditioned on a set of graph-ics codes indicating shape, position and lighting.Yang et al. (2015) added an encoder network as well as actions ...

  Image, Texts, Decoder, Synthesis, Deep, Encoder, Convolutional, Text to image synthesis, Deep convolutional decoder

On the di culty of training recurrent neural networks

On 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.

Deep Gaussian Processes

representational 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 ...

  Process, Gaussian, Gaussian process

Noise-contrastive estimation: A new estimation principle ...

ated 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.

  Into, Noise, Estimation, Contrastive, Noise contrastive estimation, Noise contrastive estima tion, Estima, Timation

Gender Shades: Intersectional Accuracy Disparities in ...

117 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).

  Enforcement, Gender, Shades, Stopped, Forcement, Stopped by law enforcement, Law en forcement, Gender shades

Deep Domain Confusion: Maximizing for Domain Invariance

can be trained for supervised adaptation, when there is a small amount of target labels available, or unsupervised adaptation, when no target labels are available. We intro-duce domain invariance through domain confusion guided selection of the depth and width of the adaptation layer, as well as an additional domain loss term during fine-tuning

  Deep, Maximizing, Confusion, Adaptation, Domain, Unsupervised, Invariance, Unsupervised adaptation, Deep domain confusion, Maximizing for domain invariance

PSD: Principled Synthetic-to-Real Dehazing Guided by ...

2.2. Unsupervised Domain Adaptation Unsupervised domain adaptation aims to tackle domain shift between source and target domains, while images in the target domain are unlabeled. One major idea is to in-duce alignment between the source and target domains in feature space by optimizing for some measurements of dis-tributional discrepancy [23 ...

  Adaptation, Unsupervised, Unsupervised adaptation

Maximum Classifier Discrepancy for Unsupervised Domain ...

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation Kuniaki Saito1, Kohei Watanabe1, Yoshitaka Ushiku1, and Tatsuya Harada1,2 1The University of Tokyo, 2RIKEN {k-saito,watanabe,ushiku,harada}@mi.t.u-tokyo.ac.jp Abstract In this work, we present a method for unsupervised do-

  Adaptation, Unsupervised

Internal Audit: Key risk areas 2021

circumvented when employees are unsupervised, as they are often overlooked and ignored to save time. Advancements of technology also increase the sophistication and frequency of cyber security attacks and frauds. Internal Audit can offer its view on the extent to which any relaxing or adaptation of controls has

  Internal, Audit, Adaptation, Internal audit, Unsupervised

Guidance on the Use of Antipsychotics

Lester UK Adaptation – Positive Cardiometabolic Health Resource 51 Appendix 7 Drugs known to prolong QT interval 54 Appendix 8 References ... who are unsupervised For non-adherent patients who are supervised Change to an alternative depot medication or other long-acting

  Adaptation, Unsupervised

WIPO Technology Trends 2019: Artificial Intelligence

“unsupervised learning” – learning without labelled data – remains a holy grail of AI. Even without this “holy grail,” AI is already creating massive economic value in the world today. In covering AI, the media tends to focus on images, speech and natural language processing because those types of data are very human.

  Technology, Trends, Wipo, Unsupervised, Wipo technology trends

AAAI-21 Accepted Paper List.1.29

! 4!! 228:!Rain!Streak!Removal!via!Dual!Graph!Convolutional!Network! Xueyang!Fu,!Qi!Qi,!Yurui!Zhu,!Xinghao!Ding,!Zheng*Jun!Zha!! 233:!RevMan:!Revenue*Aware!Multi*Task ...