Deep One-Class Classification

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

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

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

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.

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

Universal Domain Adaptation - CVF Open Access

deep adaptation methods further explore architecture de-signs [19, 43, 36, 26, 20, 41, 16, 47, 25, 4, 18]. Tzeng et al. [38] and Long et al. [21] first proposed to minimize Max-imum Mean Discrepancy (MMD) of deep features across domains. Long et al. [23] further exploits a residual trans-fer structure and introduces entropy minimization on ...

  Deep, Adaptation, Deep adaptation

Model-Agnostic Meta-Learning for Fast Adaptation of Deep ...

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks large improvements in the task loss. The primary contribution of this work is a simple model-and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradi-ent updates will lead to fast learning on a new task. We

  Deep, Adaptation

Rootstock Selection

Soil adaptation Ease of propagation Other Vigor Mineral nutrition1 characteristics Low–med. N, P: low K, Mg: low–med. Deep, well-drained, fertile, moist soils High Early maturation; scions tend to overbear High N: high P: low on low-P soils, high on high-P soils K: high Deep soils High Fruit set problems with some scions; latent virus tolerant

  Deep, Adaptation

Adversarial Discriminative Domain Adaptation

to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and test domain distributions and thus improve generalization

  Deep, Adaptation

FCCC

Further recognizes that deep cuts in global greenhouse gas emissions are required according to science, and as documented in the Fourth Assessment Report of the Inter- ... Agrees that adaptation is a challenge faced by all Parties, and that enhanced action

  Deep, Adaptation

Deep Adaptation: A Map for Navigating Climate Tragedy

Deep Adaptation: A Map for Navigating Climate Tragedy IFLAS Occasional Paper 2 www.iflas.info Professor Jem Bendell BA (Hons) PhD Originally Published July 27th 2018. Revised 2nd Edition Released July 27th 2020. Author’s note on this updated version: Two years after its first release, this paper has influenced hundreds of thousands of people to

  Deep, Adaptation, Deep adaptation

NPIAP Pressure Injury Stages - cdn.ymaws.com

Deep Tissue Pressure Injury: Persistent non-blanchable deep red, maroon or purple discoloration . Intact or non-intact skin with localized area of persistent non-blanchable deep red, maroon, purple discoloration or epidermal separation revealing a dark wound bed or blood filled blister. Pain and temperature change often precede skin color changes.

  Pressure, Injury, Deep, Pressure injury

LAYER FARMING - sikkim-ahvs.gov.in

Eggs should be collected 3-4 times a day in deep litter system of housing. Treatment may be made against external parasites like ticks, mites and lice periodically. Remove dead birds promptly and dispose them properly. Light should start from 12 hrs a day & increased by 15-30 minutes every week until 16 hrs of light is reached. FLOOR SPACE ...

  Deep

Deep Reinforcement Learning with Double Q-learning

a flexible deep neural network and was tested on a varied and large set of deterministic Atari 2600 games, reaching human-level performance on many games. In some ways, this setting is a best-case scenario for Q-learning, because the deep neural network provides flexible function approx-imation with the potential for a low asymptotic approxima-

  Learning, Deep, Double, Double q learning