Model: lufercho/AxvBert-Sentente-Transformer
Author: lufercho
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:5000, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:lufercho/my-finetuned-bert-mlm, base_model:finetune:lufercho/my-finetuned-bert-mlm, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: lufercho/my-finetuned-bert-mlm
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:5000, loss:MultipleNegativesRankingLoss
base_model: lufercho/my-finetuned-bert-mlm
widget: source_sentence: A Comprehensive Approach to Universal Piecewise Nonlinear Regression
  Based on Trees
sentences:   In sparse recovery we are given a matrix $A$ (the dictionary) and a vector of
the form $A X$ where $X$ is sparse, and the goal is to recover $X$. This is a
central notion in signal processing, statistics and machine learning. But in
applications such as sparse coding, edge detection, compression and super
resolution, the dictionary $A$ is unknown and has to be learned from random
examples of the form $Y = AX$ where $X$ is drawn from an appropriate
distribution --- this is the dictionary learning problem. In most settings, $A$
is overcomplete: it has more columns than rows. This paper presents a
polynomial-time algorithm for learning overcomplete dictionaries; the only
previously known algorithm with provable guarantees is the recent work of
Spielman, Wang and Wright who gave an algorithm for the full-rank case, which
is rarely the case in applications. Our algorithm applies to incoherent
dictionaries which have been a central object of study since they were
introduced in seminal work of Donoho and Huo. In particular, a dictionary is
$\mu$-incoherent if each pair of columns has inner product at most $\mu /
\sqrt{n}$.
  The algorithm makes natural stochastic assumptions about the unknown sparse
vector $X$, which can contain $k \leq c \min(\sqrt{n}/\mu \log n, m^{1/2
-\eta})$ non-zero entries (for any $\eta > 0$). This is close to the best $k$
allowable by the best sparse recovery algorithms even if one knows the
dictionary $A$ exactly. Moreover, both the running time and sample complexity
depend on $\log 1/\epsilon$, where $\epsilon$ is the target accuracy, and so
our algorithms converge very quickly to the true dictionary. Our algorithm can
also tolerate substantial amounts of noise provided it is incoherent with
respect to the dictionary (e.g., Gaussian). In the noisy setting, our running
time and sample complexity depend polynomially on $1/\epsilon$, and this is
necessary.
,   In this paper, we investigate adaptive nonlinear regression and introduce
tree based piecewise linear regression algorithms that are highly efficient and
provide significantly improved performance with guaranteed upper bounds in an
individual sequence manner. We use a tree notion in order to partition the
space of regressors in a nested structure. The introduced algorithms adapt not
only their regression functions but also the complete tree structure while
achieving the performance of the "best" linear mixture of a doubly exponential
number of partitions, with a computational complexity only polynomial in the
number of nodes of the tree. While constructing these algorithms, we also avoid
using any artificial "weighting" of models (with highly data dependent
parameters) and, instead, directly minimize the final regression error, which
is the ultimate performance goal. The introduced methods are generic such that
they can readily incorporate different tree construction methods such as random
trees in their framework and can use different regressor or partitioning
functions as demonstrated in the paper.
,   In this paper we propose a multi-task linear classifier learning problem
called D-SVM (Dictionary SVM). D-SVM uses a dictionary of parameter covariance
shared by all tasks to do multi-task knowledge transfer among different tasks.
We formally define the learning problem of D-SVM and show two interpretations
of this problem, from both the probabilistic and kernel perspectives. From the
probabilistic perspective, we show that our learning formulation is actually a
MAP estimation on all optimization variables. We also show its equivalence to a
multiple kernel learning problem in which one is trying to find a re-weighting
kernel for features from a dictionary of basis (despite the fact that only
linear classifiers are learned). Finally, we describe an alternative
optimization scheme to minimize the objective function and present empirical
studies to valid our algorithm.
, source_sentence: A Game-theoretic Machine Learning Approach for Revenue Maximization in
  Sponsored Search
sentences:   A learning algorithm based on primary school teaching and learning is
presented. The methodology is to continuously evaluate a student and to give
them training on the examples for which they repeatedly fail, until, they can
correctly answer all types of questions. This incremental learning procedure
produces better learning curves by demanding the student to optimally dedicate
their learning time on the failed examples. When used in machine learning, the
algorithm is found to train a machine on a data with maximum variance in the
feature space so that the generalization ability of the network improves. The
algorithm has interesting applications in data mining, model evaluations and
rare objects discovery.
,   In this paper we extend temporal difference policy evaluation algorithms to
performance criteria that include the variance of the cumulative reward. Such
criteria are useful for risk management, and are important in domains such as
finance and process control. We propose both TD(0) and LSTD(lambda) variants
with linear function approximation, prove their convergence, and demonstrate
their utility in a 4-dimensional continuous state space problem.
,   Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.
, source_sentence: Normalized Online Learning
sentences:   The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth
optimization has regained much interest in recent years in the context of large
scale optimization and machine learning. A key advantage of the method is that
it avoids projections - the computational bottleneck in many applications -
replacing it by a linear optimization step. Despite this advantage, the known
convergence rates of the FW method fall behind standard first order methods for
most settings of interest. It is an active line of research to derive faster
linear optimization-based algorithms for various settings of convex
optimization.
  In this paper we consider the special case of optimization over strongly
convex sets, for which we prove that the vanila FW method converges at a rate
of $\frac{1}{t^2}$. This gives a quadratic improvement in convergence rate
compared to the general case, in which convergence is of the order
$\frac{1}{t}$, and known to be tight. We show that various balls induced by
$\ell_p$ norms, Schatten norms and group norms are strongly convex on one hand
and on the other hand, linear optimization over these sets is straightforward
and admits a closed-form solution. We further show how several previous
fast-rate results for the FW method follow easily from our analysis.
,   We introduce online learning algorithms which are independent of feature
scales, proving regret bounds dependent on the ratio of scales existent in the
data rather than the absolute scale. This has several useful effects: there is
no need to pre-normalize data, the test-time and test-space complexity are
reduced, and the algorithms are more robust.
,   In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average.
, source_sentence: Bounds on the Bethe Free Energy for Gaussian Networks
sentences:   We extend the Bayesian Information Criterion (BIC), an asymptotic
approximation for the marginal likelihood, to Bayesian networks with hidden
variables. This approximation can be used to select models given large samples
of data. The standard BIC as well as our extension punishes the complexity of a
model according to the dimension of its parameters. We argue that the dimension
of a Bayesian network with hidden variables is the rank of the Jacobian matrix
of the transformation between the parameters of the network and the parameters
of the observable variables. We compute the dimensions of several networks
including the naive Bayes model with a hidden root node.
,   Complex networks refer to large-scale graphs with nontrivial connection
patterns. The salient and interesting features that the complex network study
offer in comparison to graph theory are the emphasis on the dynamical
properties of the networks and the ability of inherently uncovering pattern
formation of the vertices. In this paper, we present a hybrid data
classification technique combining a low level and a high level classifier. The
low level term can be equipped with any traditional classification techniques,
which realize the classification task considering only physical features (e.g.,
geometrical or statistical features) of the input data. On the other hand, the
high level term has the ability of detecting data patterns with semantic
meanings. In this way, the classification is realized by means of the
extraction of the underlying network's features constructed from the input
data. As a result, the high level classification process measures the
compliance of the test instances with the pattern formation of the training
data. Out of various high level perspectives that can be utilized to capture
semantic meaning, we utilize the dynamical features that are generated from a
tourist walker in a networked environment. Specifically, a weighted combination
of transient and cycle lengths generated by the tourist walk is employed for
that end. Interestingly, our study shows that the proposed technique is able to
further improve the already optimized performance of traditional classification
techniques.
,   We address the problem of computing approximate marginals in Gaussian
probabilistic models by using mean field and fractional Bethe approximations.
As an extension of Welling and Teh (2001), we define the Gaussian fractional
Bethe free energy in terms of the moment parameters of the approximate
marginals and derive an upper and lower bound for it. We give necessary
conditions for the Gaussian fractional Bethe free energies to be bounded from
below. It turns out that the bounding condition is the same as the pairwise
normalizability condition derived by Malioutov et al. (2006) as a sufficient
condition for the convergence of the message passing algorithm. By giving a
counterexample, we disprove the conjecture in Welling and Teh (2001): even when
the Bethe free energy is not bounded from below, it can possess a local minimum
to which the minimization algorithms can converge.
, source_sentence: Multi-Armed Bandits in Metric Spaces
sentences:   The paper presents a FrameNet-based information extraction and knowledge
representation framework, called FrameNet-CNL. The framework is used on natural
language documents and represents the extracted knowledge in a tailor-made
Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be
generated automatically in multiple languages. This approach brings together
the fields of information extraction and CNL, because a source text can be
considered belonging to FrameNet-CNL, if information extraction parser produces
the correct knowledge representation as a result. We describe a
state-of-the-art information extraction parser used by a national news agency
and speculate that FrameNet-CNL eventually could shape the natural language
subset used for writing the newswire articles.
,   Applications such as face recognition that deal with high-dimensional data
need a mapping technique that introduces representation of low-dimensional
features with enhanced discriminatory power and a proper classifier, able to
classify those complex features. Most of traditional Linear Discriminant
Analysis suffer from the disadvantage that their optimality criteria are not
directly related to the classification ability of the obtained feature
representation. Moreover, their classification accuracy is affected by the
"small sample size" problem which is often encountered in FR tasks. In this
short paper, we combine nonlinear kernel based mapping of data called KDDA with
Support Vector machine classifier to deal with both of the shortcomings in an
efficient and cost effective manner. The proposed here method is compared, in
terms of classification accuracy, to other commonly used FR methods on UMIST
face database. Results indicate that the performance of the proposed method is
overall superior to those of traditional FR approaches, such as the Eigenfaces,
Fisherfaces, and D-LDA methods and traditional linear classifiers.
,   In a multi-armed bandit problem, an online algorithm chooses from a set of
strategies in a sequence of trials so as to maximize the total payoff of the
chosen strategies. While the performance of bandit algorithms with a small
finite strategy set is quite well understood, bandit problems with large
strategy sets are still a topic of very active investigation, motivated by
practical applications such as online auctions and web advertisement. The goal
of such research is to identify broad and natural classes of strategy sets and
payoff functions which enable the design of efficient solutions. In this work
we study a very general setting for the multi-armed bandit problem in which the
strategies form a metric space, and the payoff function satisfies a Lipschitz
condition with respect to the metric. We refer to this problem as the
"Lipschitz MAB problem". We present a complete solution for the multi-armed
problem in this setting. That is, for every metric space (L,X) we define an
isometry invariant which bounds from below the performance of Lipschitz MAB
algorithms for X, and we present an algorithm which comes arbitrarily close to
meeting this bound. Furthermore, our technique gives even better results for
benign payoff functions.

pipeline_tag: sentence-similarity
library_name: sentence-transformers
Likes: 0 | All-time downloads: 10
Created: 2024-11-18 21:08:51