Model: lufercho/my-finetuned-sentence-bert
Author: lufercho
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:5000, loss:TripletLoss, arxiv:1908.10084, arxiv:1703.07737, 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:TripletLoss
base_model: lufercho/my-finetuned-bert-mlm
widget: source_sentence: Auto-WEKA: Combined Selection and Hyperparameter Optimization of
  Classification Algorithms
sentences:   It has been a long time, since data mining technologies have made their ways
to the field of data management. Classification is one of the most important
data mining tasks for label prediction, categorization of objects into groups,
advertisement and data management. In this paper, we focus on the standard
classification problem which is predicting unknown labels in Euclidean space.
Most efforts in Machine Learning communities are devoted to methods that use
probabilistic algorithms which are heavy on Calculus and Linear Algebra. Most
of these techniques have scalability issues for big data, and are hardly
parallelizable if they are to maintain their high accuracies in their standard
form. Sampling is a new direction for improving scalability, using many small
parallel classifiers. In this paper, rather than conventional sampling methods,
we focus on a discrete classification algorithm with O(n) expected running
time. Our approach performs a similar task as sampling methods. However, we use
column-wise sampling of data, rather than the row-wise sampling used in the
literature. In either case, our algorithm is completely deterministic. Our
algorithm, proposes a way of combining 2D convex hulls in order to achieve high
classification accuracy as well as scalability in the same time. First, we
thoroughly describe and prove our O(n) algorithm for finding the convex hull of
a point set in 2D. Then, we show with experiments our classifier model built
based on this idea is very competitive compared with existing sophisticated
classification algorithms included in commercial statistical applications such
as MATLAB.
,   Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that addresses these issues in isolation. We show that this problem can be
addressed by a fully automated approach, leveraging recent innovations in
Bayesian optimization. Specifically, we consider a wide range of feature
selection techniques (combining 3 search and 8 evaluator methods) and all
classification approaches implemented in WEKA, spanning 2 ensemble methods, 10
meta-methods, 27 base classifiers, and hyperparameter settings for each
classifier. On each of 21 popular datasets from the UCI repository, the KDD Cup
09, variants of the MNIST dataset and CIFAR-10, we show classification
performance often much better than using standard selection/hyperparameter
optimization methods. We hope that our approach will help non-expert users to
more effectively identify machine learning algorithms and hyperparameter
settings appropriate to their applications, and hence to achieve improved
performance.
,   Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data
analysis. An important variant is the sparse NMF problem which arises when we
explicitly require the learnt features to be sparse. A natural measure of
sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms,
such as L$_1$/L$_2$ measure, have been shown to model sparsity robustly, based
on intuitive attributes that such measures need to satisfy. This is in contrast
to computationally cheaper alternatives such as the plain L$_1$ norm. However,
present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow
and other formulations for sparse NMF have been proposed such as those based on
L$_1$ and L$_0$ norms. Our proposed algorithm allows us to solve the mixed norm
sparsity constraints while not sacrificing computation time. We present
experimental evidence on real-world datasets that shows our new algorithm
performs an order of magnitude faster compared to the current state-of-the-art
solvers optimizing the mixed norm and is suitable for large-scale datasets.
, source_sentence: Effect of Different Distance Measures on the Performance of K-Means
  Algorithm: An Experimental Study in Matlab
sentences:   The kernel method is a potential approach to analyzing structured data such
as sequences, trees, and graphs; however, unordered trees have not been
investigated extensively. Kimura et al. (2011) proposed a kernel function for
unordered trees on the basis of their subpaths, which are vertical
substructures of trees responsible for hierarchical information in them. Their
kernel exhibits practically good performance in terms of accuracy and speed;
however, linear-time computation is not guaranteed theoretically, unlike the
case of the other unordered tree kernel proposed by Vishwanathan and Smola
(2003). In this paper, we propose a theoretically guaranteed linear-time kernel
computation algorithm that is practically fast, and we present an efficient
prediction algorithm whose running time depends only on the size of the input
tree. Experimental results show that the proposed algorithms are quite
efficient in practice.
,   We express the classic ARMA time-series model as a directed graphical model.
In doing so, we find that the deterministic relationships in the model make it
effectively impossible to use the EM algorithm for learning model parameters.
To remedy this problem, we replace the deterministic relationships with
Gaussian distributions having a small variance, yielding the stochastic ARMA
(ARMA) model. This modification allows us to use the EM algorithm to learn
parmeters and to forecast,even in situations where some data is missing. This
modification, in conjunction with the graphicalmodel approach, also allows us
to include cross predictors in situations where there are multiple times series
and/or additional nontemporal covariates. More surprising,experiments suggest
that the move to stochastic ARMA yields improved accuracy through better
smoothing. We demonstrate improvements afforded by cross prediction and better
smoothing on real data.
,   K-means algorithm is a very popular clustering algorithm which is famous for
its simplicity. Distance measure plays a very important rule on the performance
of this algorithm. We have different distance measure techniques available. But
choosing a proper technique for distance calculation is totally dependent on
the type of the data that we are going to cluster. In this paper an
experimental study is done in Matlab to cluster the iris and wine data sets
with different distance measures and thereby observing the variation of the
performances shown.
, source_sentence: A Dynamic Near-Optimal Algorithm for Online Linear Programming
sentences:   Social media channels such as Twitter have emerged as popular platforms for
crowds to respond to public events such as speeches, sports and debates. While
this promises tremendous opportunities to understand and make sense of the
reception of an event from the social media, the promises come entwined with
significant technical challenges. In particular, given an event and an
associated large scale collection of tweets, we need approaches to effectively
align tweets and the parts of the event they refer to. This in turn raises
questions about how to segment the event into smaller yet meaningful parts, and
how to figure out whether a tweet is a general one about the entire event or
specific one aimed at a particular segment of the event. In this work, we
present ET-LDA, an effective method for aligning an event and its tweets
through joint statistical modeling of topical influences from the events and
their associated tweets. The model enables the automatic segmentation of the
events and the characterization of tweets into two categories: (1) episodic
tweets that respond specifically to the content in the segments of the events,
and (2) steady tweets that respond generally about the events. We present an
efficient inference method for this model, and a comprehensive evaluation of
its effectiveness over existing methods. In particular, through a user study,
we demonstrate that users find the topics, the segments, the alignment, and the
episodic tweets discovered by ET-LDA to be of higher quality and more
interesting as compared to the state-of-the-art, with improvements in the range
of 18-41%.
,   A natural optimization model that formulates many online resource allocation
and revenue management problems is the online linear program (LP) in which the
constraint matrix is revealed column by column along with the corresponding
objective coefficient. In such a model, a decision variable has to be set each
time a column is revealed without observing the future inputs and the goal is
to maximize the overall objective function. In this paper, we provide a
near-optimal algorithm for this general class of online problems under the
assumption of random order of arrival and some mild conditions on the size of
the LP right-hand-side input. Specifically, our learning-based algorithm works
by dynamically updating a threshold price vector at geometric time intervals,
where the dual prices learned from the revealed columns in the previous period
are used to determine the sequential decisions in the current period. Due to
the feature of dynamic learning, the competitiveness of our algorithm improves
over the past study of the same problem. We also present a worst-case example
showing that the performance of our algorithm is near-optimal.
,   One of the biggest challenges in Multimedia information retrieval and
understanding is to bridge the semantic gap by properly modeling concept
semantics in context. The presence of out of vocabulary (OOV) concepts
exacerbates this difficulty. To address the semantic gap issues, we formulate a
problem on learning contextualized semantics from descriptive terms and propose
a novel Siamese architecture to model the contextualized semantics from
descriptive terms. By means of pattern aggregation and probabilistic topic
models, our Siamese architecture captures contextualized semantics from the
co-occurring descriptive terms via unsupervised learning, which leads to a
concept embedding space of the terms in context. Furthermore, the co-occurring
OOV concepts can be easily represented in the learnt concept embedding space.
The main properties of the concept embedding space are demonstrated via
visualization. Using various settings in semantic priming, we have carried out
a thorough evaluation by comparing our approach to a number of state-of-the-art
methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500
and Million Song Dataset in the music domain as well as Corel5K, LabelMe and
SUNDatabase in the image domain. Experimental results on semantic priming
suggest that our approach outperforms those state-of-the-art methods
considerably in various aspects.
, source_sentence: Parallel Online Learning
sentences:   In our recent paper, we showed that in exponential family, contrastive
divergence (CD) with fixed learning rate will give asymptotically consistent
estimates \cite{wu2016convergence}. In this paper, we establish consistency and
convergence rate of CD with annealed learning rate $\eta_t$. Specifically,
suppose CD-$m$ generates the sequence of parameters $\{\theta_t\}_{t \ge 0}$
using an i.i.d. data sample $\mathbf{X}_1^n \sim p_{\theta^*}$ of size $n$,
then $\delta_n(\mathbf{X}_1^n) = \limsup_{t \to \infty} \Vert \sum_{s=t_0}^t
\eta_s \theta_s / \sum_{s=t_0}^t \eta_s - \theta^* \Vert$ converges in
probability to 0 at a rate of $1/\sqrt[3]{n}$. The number ($m$) of MCMC
transitions in CD only affects the coefficient factor of convergence rate. Our
proof is not a simple extension of the one in \cite{wu2016convergence}. which
depends critically on the fact that $\{\theta_t\}_{t \ge 0}$ is a homogeneous
Markov chain conditional on the observed sample $\mathbf{X}_1^n$. Under
annealed learning rate, the homogeneous Markov property is not available and we
have to develop an alternative approach based on super-martingales. Experiment
results of CD on a fully-visible $2\times 2$ Boltzmann Machine are provided to
demonstrate our theoretical results.
,   This report outlines an approach to learning generative models from data. We
express models as probabilistic programs, which allows us to capture abstract
patterns within the examples. By choosing our language for programs to be an
extension of the algebraic data type of the examples, we can begin with a
program that generates all and only the examples. We then introduce greater
abstraction, and hence generalization, incrementally to the extent that it
improves the posterior probability of the examples given the program. Motivated
by previous approaches to model merging and program induction, we search for
such explanatory abstractions using program transformations. We consider two
types of transformation: Abstraction merges common subexpressions within a
program into new functions (a form of anti-unification). Deargumentation
simplifies functions by reducing the number of arguments. We demonstrate that
this approach finds key patterns in the domain of nested lists, including
parameterized sub-functions and stochastic recursion.
,   In this work we study parallelization of online learning, a core primitive in
machine learning. In a parallel environment all known approaches for parallel
online learning lead to delayed updates, where the model is updated using
out-of-date information. In the worst case, or when examples are temporally
correlated, delay can have a very adverse effect on the learning algorithm.
Here, we analyze and present preliminary empirical results on a set of learning
architectures based on a feature sharding approach that present various
tradeoffs between delay, degree of parallelism, representation power and
empirical performance.
, source_sentence: Maximin affinity learning of image segmentation
sentences:   Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. Here we propose a flexible yet simple framework that is able to
accommodate different types of loss functions and hash functions. This
framework allows a number of existing approaches to hashing to be placed in
context, and simplifies the development of new problem-specific hashing
methods. Our framework decomposes hashing learning problem into two steps: hash
bit learning and hash function learning based on the learned bits. The first
step can typically be formulated as binary quadratic problems, and the second
step can be accomplished by training standard binary classifiers. Both problems
have been extensively studied in the literature. Our extensive experiments
demonstrate that the proposed framework is effective, flexible and outperforms
the state-of-the-art.
,   Changes in the UK electricity market mean that domestic users will be
required to modify their usage behaviour in order that supplies can be
maintained. Clustering allows usage profiles collected at the household level
to be clustered into groups and assigned a stereotypical profile which can be
used to target marketing campaigns. Fuzzy C Means clustering extends this by
allowing each household to be a member of many groups and hence provides the
opportunity to make personalised offers to the household dependent on their
degree of membership of each group. In addition, feedback can be provided on
how user's changing behaviour is moving them towards more "green" or cost
effective stereotypical usage.
,   Images can be segmented by first using a classifier to predict an affinity
graph that reflects the degree to which image pixels must be grouped together
and then partitioning the graph to yield a segmentation. Machine learning has
been applied to the affinity classifier to produce affinity graphs that are
good in the sense of minimizing edge misclassification rates. However, this
error measure is only indirectly related to the quality of segmentations
produced by ultimately partitioning the affinity graph. We present the first
machine learning algorithm for training a classifier to produce affinity graphs
that are good in the sense of producing segmentations that directly minimize
the Rand index, a well known segmentation performance measure. The Rand index
measures segmentation performance by quantifying the classification of the
connectivity of image pixel pairs after segmentation. By using the simple graph
partitioning algorithm of finding the connected components of the thresholded
affinity graph, we are able to train an affinity classifier to directly
minimize the Rand index of segmentations resulting from the graph partitioning.
Our learning algorithm corresponds to the learning of maximin affinities
between image pixel pairs, which are predictive of the pixel-pair connectivity.

pipeline_tag: sentence-similarity
library_name: sentence-transformers
Likes: 0 | All-time downloads: 6
Created: 2024-11-18 19:23:44