- Last 7 days
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netflixtechblog.com netflixtechblog.com
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how artwork performs in relation to other artwork we select in the same page or session
This is the slate optimization problem.
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when presenting a specific piece of artwork for a title influenced a member to play (or not to play) a title and when a member would have played a title (or not) regardless of which image we presented
So to establish some causality between the thumbnail shown and the user's viewing of the show.
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- Nov 2020
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iwww.corp.linkedin.com iwww.corp.linkedin.com
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even two members characterized by the similar sets of features described above may still have different preferences over jobs, due to the fact that some intrinsic difference between those two members may not be well captured by the features
So this brings the question on how GLMix can capture this difference if the data cannot already capture the difference?
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such individual-specific effects among different populations (e.g., members, jobs) are ignored in GLM
Because they are drowned out by the broader pattern?
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- Apr 2020
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ai.googleblog.com ai.googleblog.com
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based on logged experiences of a DQN agent
So a different DQN agent is used to generate the logging data?
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- Aug 2019
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en.wikipedia.org en.wikipedia.org
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As log-bilinear regression model for unsupervised learning of word representations, it combines the features of two model families, namely the global matrix factorization and local context window methods
What does "log-bilinear regression" mean exactly?
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- Jul 2019
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en.wikipedia.org en.wikipedia.org
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An Oblivious Tree is a rooted tree with the following property: All the leaves are in the same level. All the internal nodes have degree at most 3. Only the nodes along the rightmost path in the tree may have degree of one.
Note this is not the definition of the oblivious decision trees in the CatBoost paper.
There a oblivious decision tree means a tree where the feature used for splitting is the same across all intermediate nodes within the same level of the tree, and the leaves are all in the same level.
See: https://stats.stackexchange.com/questions/353172/what-is-oblivious-decision-tree-and-why
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- Jun 2019
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arxiv.org arxiv.org
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features (sparse)
are these feature values or actual features?
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Note that thescalar multipledoes not meanxkis linear withx0
x_k is not a linear function of x_0
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We argue that the CrossNet learns a special typeof high-order feature interactions, where each hidden layer in theCrossNet is a scalar multiple ofx0
In that case CrossNet doesn't really learn anything?
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multivalent,
takes on more than one value
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univalent,
takes on a unique value
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Annotators
URL
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- Mar 2019
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heap.sort() maintains the heap invariant
may swap the indices of the nodes at the same height but will keep the sorted array a min heap
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The most common error I see is a subconscious assumption that each word can have at most one synonym
Use sets as the value.
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- Oct 2018
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en.wikipedia.org en.wikipedia.org
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The perplexity of the model q is defined as b − 1 N ∑ i = 1 N log b q ( x i ) {\displaystyle b^{-{\frac {1}{N}}\sum _{i=1}^{N}\log _{b}q(x_{i})}}
The perplexity formula is missing the probability distribution \(p\)
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en.wikipedia.org en.wikipedia.org
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It has been demonstrated that this formulation is almost equivalent to a SLIM model,[9] which is an item-item model based recommender
So a pre-trained item model can be used to make such recommendations.
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The user's latent factors represent the preference of that user for the corresponding item's latent factors
The higher the value of the dot product between the two, the higher the preference.
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two lower dimensional matrices
Not necessary (in fact, often not) square. Typically each user is represented by a vector of dimension strictly less than the number of items and vice versa.
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karpathy.github.io karpathy.github.io
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are are
*are
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redis.io redis.io
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it will not try to start a failover if the master link was disconnected for more than the specified amount of time
Why would it exhibit this behavior? Is it because a slave that's disconnected from the master for too long has stale data? Or is it because the slave made be failing as well?
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mp.weixin.qq.com mp.weixin.qq.com机器之心2
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会话
Session
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Python
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- Sep 2018
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conditional distribution for individual components can be constructed
So the conditional distribution is conditioned on other components?
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p(y∣x)=∫p(y∣f,x)p(f∣x)df
\(y\) is the data, \(f\) is the model, \(x\) is the input variable
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am207.github.io am207.github.io
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marginaly
*marginal
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corvariance
*covariance
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y=y1,…,yn=m
\(n = m\)
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am207.github.io am207.github.io
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must store an amount of information which increases with the size of the data
Or you can use MCMC.
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some
*sum
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calculation once again involves inverting a NxN matrix as in the kernel space representation of regression
this is why we use MCMC or other distribution sampling technique instead
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$f(x_)foratestvectorinputforatestvectorinput for a test vector input x_,givenatrainingsetXwithvaluesyfortheGPisonceagainagaussiangivenbyequationCwithameanvector,givenatrainingsetXwithvaluesyfortheGPisonceagainagaussiangivenbyequationCwithameanvector, given a training set X with values y for the GP is once again a gaussian given by equation C with a mean vector m_andcovariancematrixandcovariancematrix and covariance matrix k_$:
...$f(x)$ for a test vector input $x$, given a training set $X$ with values $y$ for the GP is once again a gaussian given by equation C with a mean vector $m$ and covariance matrix $k$:
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corvariance
*covariance
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in equation B for the marginal of a gaussian, only the covariance of the block of the matrix involving the unmarginalized dimensions matters! Thus “if you ask only for the properties of the function (you are fitting to the data) at a finite number of points, then inference in the Gaussian process will give you the same answer if you ignore the infinitely many other points, as if you would have taken them all into account!”(Rasmunnsen)
key insight into Gaussian processes
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they
*the
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im
*in
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Notice now that the features only appear in the combination κ(x,x′)=xTΣx′,κ(x,x′)=xTΣx′,\kappa(x,x') = x^T \Sigma x', thus leading to writing the posterior predictive as p(f(x∗)|x∗,X,y)=N(κ(x∗,X)(κ(XT,X)+σ2I)−1y,κ(x∗,x∗)−κ(x∗,XT)(κ(XT,X)+σ2I)−1κ(XT,x∗))p(f(x∗)|x∗,X,y)=N(κ(x∗,X)(κ(XT,X)+σ2I)−1y,κ(x∗,x∗)−κ(x∗,XT)(κ(XT,X)+σ2I)−1κ(XT,x∗))p(f(x_*) | x_* , X, y) = N\left(\kappa(x_*,X) \left(\kappa(X^T,X) + \sigma^2 I\right)^{-1}y,\,\,\, \kappa(x_*,x_*) - \kappa(x_*,X^T)\left(\kappa(X^T,X) + \sigma^2 I\right)^{-1} \kappa(X^T,x_*) \right) The function κκ\kappa is called the kernel
how the kernel came about?
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am207.github.io am207.github.io
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generate
more like "sample"
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f∗
\(f^*\) denotes the model
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Bayesian approach to handling observation noise
One core contribution of this work.
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mp.weixin.qq.com mp.weixin.qq.com腾讯AI实验室1
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通道剪枝算法
channel pruning algorithm
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- Aug 2018
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en.wikipedia.org en.wikipedia.org
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expected values change during the series
So no longer identically distributed
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www.fast.ai www.fast.ai
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To learn a network for Cifar-10, DARTS takes just 4 GPU days, compared to 1800 GPU days for NASNet and 3150 GPU days for AmoebaNet
What about in comparison to ENAS?
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- Jul 2018
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spark.apache.org spark.apache.org
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partitioner
How to define a partitioner?
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mp.weixin.qq.com mp.weixin.qq.comSigAI3
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极大极小(Max-min)博弈
Choose D to maximally discriminate D vs G and at the same time learn the real data; choose G to best "confuse" D.
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交叉熵
Cross entropy
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零和博弈
Zero-sum game
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- Jun 2018
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people.cs.bris.ac.uk people.cs.bris.ac.uk
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isometrics in ROC space
What does this mean exactly?
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Local file Local file
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you will have to do a lot of work to select appropriate input data and to code the data as numeric values
Not really anymore with the advent of convolutional neural networks.
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ai.intel.com ai.intel.com
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next state s’
Is the next state s' the state reached by taking the action with the highest reward?
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- May 2018
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blog.cloudera.com blog.cloudera.com
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The number of tasks is the single most important parameter.
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