112,026 research outputs found
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
Transfer learning has fundamentally changed the landscape of natural language
processing (NLP) research. Many existing state-of-the-art models are first
pre-trained on a large text corpus and then fine-tuned on downstream tasks.
However, due to limited data resources from downstream tasks and the extremely
large capacity of pre-trained models, aggressive fine-tuning often causes the
adapted model to overfit the data of downstream tasks and forget the knowledge
of the pre-trained model. To address the above issue in a more principled
manner, we propose a new computational framework for robust and efficient
fine-tuning for pre-trained language models. Specifically, our proposed
framework contains two important ingredients: 1. Smoothness-inducing
regularization, which effectively manages the capacity of the model; 2. Bregman
proximal point optimization, which is a class of trust-region methods and can
prevent knowledge forgetting. Our experiments demonstrate that our proposed
method achieves the state-of-the-art performance on multiple NLP benchmarks.Comment: The 58th annual meeting of the Association for Computational
Linguistics (ACL 2020
Can body mass index influence the fracture zone in the fifth metatarsal base? A retrospective review
Fifth metatarsal base fracture are common in routine orthopaedic practice [1ā6]. Lawrence and Botte [7] pro- posed a classification based upon the position of the fracture line (zone 1: tuberosity, zone 2: meta-diaphyseal junction, zone 3: proximal diaphysis). Pathomechani- cally, injury patterns develop in different ways: in zone 1, a traction injury caused by peroneus brevis tendon and the lateral band of the plantar fascia determine an avul- sion fracture of the tuberosity, also called āpseudo-Jonesā āfracture; in zone 2, forced foot adduction and excessive plantar flexion determine a fracture in the metaphyseal- diaphyseal junction, also called Jonesā fracture [8, 9]; in zone 3, acute over-bearing onto the area or chronic overload determine a fracture in the proximal portion of the diaphysis, distal to the intermetatarsal joint [10, 11].
To the best of the Authorsā knowledge, no study has been published to date on the relationship between the value of Body Mass Index (BMI) and the prevalence of fractures in a specific portion of the fifth metatarsal base. The aim of this study was to define the impact of BMI on fifth metatarsal base fractures location according to Lawrence and Botte classification [7]
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Extending twin support vector machine classifier for multi-category classification problems
Ā© 2013 ā IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification
problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant
of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative
Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)
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