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.

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

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

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

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

Natural Language Processing (Almost) from Scratch

We propose a unified neural network architecture and learnin g algorithm that can be applied to var- ... basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for ... They perform dynamic programming at test time. Later, they improved their results up to 93.91% (Kudo and Matsumoto, 2001) using an

  Language, Dynamics, Processing, Natural, Basis, Neural, Natural language processing

Introduction to Artificial Intelligence - Donald Bren School of ...

Neural networks that learn ... is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a ... • Static (vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic

  Dynamics, Basis, Neural

Abstract - arXiv.org e-Print archive

DRL framework is powerful in solving dynamic decision making problems by learn- ... since deep neural networks are significantly powerful at approximating the expected return at a state with a certain action. With the development of more ... hourlyor on a minute basis. Action space A. The action space describes the allowed actions that the ...

  Dynamics, Basis, Neural

Long-Tailed Classification by Keeping the Good and …

34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. ... = 0:9, where iis ranking from head to tail. (c) The relative change of the performance on the basis of = 0:98 shows that the few-shot tail is more vulnerable to the momentum. ... the dynamic curriculum learning [33] and the transferring memory ...

  Dynamics, Basis, Neural

EXAM QUESTIONS CLASSIFICATION BASED ON BLOOM’S …

on the basis of Bloom’s taxonomy. This technique is not as dynamic as machine learning techniques, but nonetheless exhibits satisfactory performance. Rule-based techniques are not only time consuming and expensive to implement but are also not as robust as other techniques, such as machine learning.

  Dynamics, Basis

Sustainability: Definition and Five Core Principles

ecosystems. This alignment represents a particular type of dynamic equilibrium in the interaction between a population and the carrying capacity of its environment. It is this specific equilibrium that must be the focus of a meaningful definition of sustainability. Currently, the prevailing definition of sustainability emphasizes cross-generational

  Definition, Principles, Dynamics, Core, Sustainability, Five, Definition and five core principles

Educational Benefits of Music in an Inclusive Classroom

2. Provide more than one neural pathway by using multi-sensory channels. 3. Use visual, auditory and kinesthetic (striking a drum, clapping hands). 4. Use the inherent structure in songs to reinforce a sense of internal order. 5. Use rhythm, steady pulse, and basic beat of music as a model to help student to expe rience order, sequence, and a

  Neural

1 Basics of Cell Signaling - Wiley-VCH

1.1). The basis for the coordination of the physiological functions within a multicellular organism is intercellular signaling (or intercel-lular communication), which allows a single cell to influence the be-havior of other cells in a specific manner. As compared to single-cell organisms, where all cells behave similarly within a broad frame,

  Basis

PREDICTION OF HEART DISEASE USING MACHINE LEARNING …

We have applied following two commonly used classifiers for prediction on the basis of their performance. These classifiers are as follows: Support Vector Machine, K-Nearest Neighbor II. Dataset Description We performed computer simulation on one dataset. Dataset is a Heart dataset. The dataset is available in UCI Machine Learning Repository [10].

  Basis