a

argeting user return as the objectivedoes not significantly affect user engagement (e.g., the actualfuture user return and churning risk) and shows a trend ofhurting perception metrics compared with the baseline

user return

Gramian trick

m|Ic|q(j|c) ̃α(c,j)

samplesmnegative

For example, if the batch containspositive observations (c1,i1),(c2,i2),..., then{i1,i2,...}are treated as the neg-atives for this batch, e.g., forc1, the negatives would be (c1,i2),(c1,i3)

a ground truth set of relevant items{i1,i2,...}

weak negatives are derived from all the remaining items

heseembedding matrices,W∈RC×dandH∈RI×dare the model parametersθ

This modeling choice is consistentwith the idea of ranking the documents with largest scores first; intuitively, the more documents ina permutation are in the decreasing order of score, the bigger the probability of the permutation is.

Proof.

Zkis the normalization factor

F(d, q)the ranking function thattakes a document-query pair(d, q)

The listwise approaches can be classified into two categorie

main difficulty in optimizing these evaluation metrics is that they aredependent on the rank position of documents induced by the ranking function, not the numericalvalues output by the ranking function.

We propose a probabilisticframework that addresses this challenge by optimizing the expectation of NDCGover all the possible permutations of documents

learning to learn, or meta-learning

assign an energy (that is, a badness)

Its meaning resides in its relationships to other symbols which can be represented by a set of symbolic expressions or by a relational graph

how artwork performs in relation to other artwork we select in the same page or session

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

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

such individual-specific effects among different populations (e.g., members, jobs) are ignored in GLM

based on logged experiences of a DQN agent

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

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.

features (sparse)

Note that thescalar multipledoes not meanxkis linear withx0

We argue that the CrossNet learns a special typeof high-order feature interactions, where each hidden layer in theCrossNet is a scalar multiple ofx0

multivalent,

univalent,

heap.sort() maintains the heap invariant

The most common error I see is a subconscious assumption that each word can have at most one synonym

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})}}

It has been demonstrated that this formulation is almost equivalent to a SLIM model,[9] which is an item-item model based recommender

The user's latent factors represent the preference of that user for the corresponding item's latent factors

two lower dimensional matrices

are are

it will not try to start a failover if the master link was disconnected for more than the specified amount of time

会话

conditional distribution for individual components can be constructed

p(y∣x)=∫p(y∣f,x)p(f∣x)df

marginaly

corvariance

y=y1,…,yn=m

must store an amount of information which increases with the size of the data

some

calculation once again involves inverting a NxN matrix as in the kernel space representation of regression

$f(x_)foratestvectorinputforatestvectorinput for a test vector input x_,givenatrainingsetXwithvaluesyfortheGPisonceagainagaussiangivenbyequationCwithameanvector,givenatrainingsetXwithvaluesyfortheGPisonceagainagaussiangivenbyequationCwithameanvector, given a training set X with values y for the GP is once again a gaussian given by equation C with a mean vector m_andcovariancematrixandcovariancematrix and covariance matrix k_$:

corvariance

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)

they

im

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

generate

f∗

Bayesian approach to handling observation noise

通道剪枝算法

expected values change during the series

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

partitioner

极大极小(Max-min)博弈

交叉熵

零和博弈

isometrics in ROC space

you will have to do a lot of work to select appropriate input data and to code the data as numeric values

next state s’