scholarly journals Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

2020 ◽  
Vol 34 (04) ◽  
pp. 4844-4851
Author(s):  
Fanghui Liu ◽  
Xiaolin Huang ◽  
Yudong Chen ◽  
Jie Yang ◽  
Johan Suykens
Keyword(s):  
State Of The Art ◽  
Sampling Strategy ◽  
Prediction Performance ◽  
Current State ◽  
Kernel Approximation ◽  
Sampling Process ◽  
New Strategy ◽  
The Matrix ◽  
Benchmark Datasets ◽  
Random Fourier Features

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2+s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019).

scholarly journals Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

2021 ◽  
Author(s):  
Shengqiong Wu ◽  
Hao Fei ◽  
Yafeng Ren ◽  
Donghong Ji ◽  
Jingye Li
Keyword(s):  
State Of The Art ◽  
Boundary Detection ◽  
High Order ◽  
Experimental Results ◽  
Convolutional Network ◽  
Syntactic Knowledge ◽  
Current State ◽  
Syntactic Features ◽  
Benchmark Datasets

In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifiedly, and a local-attention module encoding POS tags for better term boundary detection. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring. Experimental results on four benchmark datasets demonstrate that our model outperforms current state-of-the-art baselines, meanwhile yielding explainable predictions with syntactic knowledge.

scholarly journals Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding

2021 ◽  
Vol 11 (5) ◽  
pp. 2371
Author(s):  
Junjian Zhan ◽  
Feng Li ◽  
Yang Wang ◽  
Daoyu Lin ◽  
Guangluan Xu
Keyword(s):  
State Of The Art ◽  
Global Information ◽  
Network Embedding ◽  
Sampling Process ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Adversarial Training ◽  
Low Dimensional ◽  
Embedding Methods ◽  
Local Proximity

As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.

scholarly journals Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2983 ◽  
Author(s):  
Tiago Oliveira ◽  
Ana Silva ◽  
Ken Satoh ◽  
Vicente Julian ◽  
Pedro Leão ◽  
...  
Keyword(s):  
Health Care ◽  
Continuous Improvement ◽  
Health Care Professionals ◽  
Prediction Models ◽  
State Of The Art ◽  
Prediction Performance ◽  
Decision Making Process ◽  
Current State ◽  
The One ◽  
Colorectal Cancer Patients

Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes.

scholarly journals Learning semantic sentence representations from visually grounded language without lexical knowledge

2019 ◽  
Vol 25 (4) ◽  
pp. 451-466 ◽  
Author(s):  
Danny Merkx ◽  
Stefan L. Frank
Keyword(s):  
Visual Information ◽  
State Of The Art ◽  
Approaches To Learning ◽  
Semantic Content ◽  
Lexical Knowledge ◽  
Multimodal Data ◽  
Word Level ◽  
Current State ◽  
Sentence Level ◽  
Benchmark Datasets

AbstractCurrent approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state of the art on two popular image-caption retrieval benchmark datasets: Microsoft Common Objects in Context (MSCOCO) and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity (STS) benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics.

scholarly journals The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Pablo Barros ◽  
Nikhil Churamani ◽  
Alessandra Sciutti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Facial Features ◽  
Expression Recognition ◽  
Current State ◽  
Benchmark Datasets

AbstractCurrent state-of-the-art models for automatic facial expression recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and, thus, improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapts the learned facial features towards the different datasets.

scholarly journals Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

2021 ◽  
Vol 9 ◽  
pp. 774-789
Author(s):  
Daniel Deutsch ◽  
Tania Bedrax-Weiss ◽  
Dan Roth
Keyword(s):  
Gold Standard ◽  
Question Answering ◽  
State Of The Art ◽  
Careful Analysis ◽  
Performance Bottlenecks ◽  
Content Quality ◽  
Current State ◽  
Benchmark Datasets ◽  
Evaluation Metric

Abstract A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference. Traditional text overlap based metrics such as ROUGE fail to achieve this because they are limited to matching tokens, either lexically or via embeddings. In this work, we propose a metric to evaluate the content quality of a summary using question-answering (QA). QA-based methods directly measure a summary’s information overlap with a reference, making them fundamentally different than text overlap metrics. We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval. QAEval outperforms current state-of-the-art metrics on most evaluations using benchmark datasets, while being competitive on others due to limitations of state-of-the-art models. Through a careful analysis of each component of QAEval, we identify its performance bottlenecks and estimate that its potential upper-bound performance surpasses all other automatic metrics, approaching that of the gold-standard Pyramid Method.1

scholarly journals Local conformal autoencoder for standardized data coordinates

2020 ◽  
Vol 117 (49) ◽  
pp. 30918-30927
Author(s):  
Erez Peterfreund ◽  
Ofir Lindenbaum ◽  
Felix Dietrich ◽  
Tom Bertalan ◽  
Matan Gavish ◽  
...  
Keyword(s):  
Latent Variables ◽  
Nonlinear Deformation ◽  
State Of The Art ◽  
Three Dimensional ◽  
Sampling Strategy ◽  
Curved Surfaces ◽  
Geometric Information ◽  
Current State ◽  
Instrumental Observations ◽  
Localization Data

We propose a local conformal autoencoder (LOCA) for standardized data coordinates. LOCA is a deep learning-based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from an unknown, nonlinear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized, latent variables. We assume a repeated measurement sampling strategy, common in scientific measurements, and present a method for learning an embedding inRdthat is isometric to the latent variables of the manifold. The coordinates recovered by our method are invariant to diffeomorphisms of the manifold, making it possible to match between different instrumental observations of the same phenomenon. Our embedding is obtained using LOCA, which is an algorithm that learns to rectify deformations by using a local z-scoring procedure, while preserving relevant geometric information. We demonstrate the isometric embedding properties of LOCA in various model settings and observe that it exhibits promising interpolation and extrapolation capabilities, superior to the current state of the art. Finally, we demonstrate LOCA’s efficacy in single-site Wi-Fi localization data and for the reconstruction of three-dimensional curved surfaces from two-dimensional projections.

A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition

2021 ◽  
Vol 5 (1) ◽  
pp. 1-26
Author(s):  
Yingwei Zhang ◽  
Yiqiang Chen ◽  
Hanchao Yu ◽  
Xiaodong Yang ◽  
Ruizhe Sun ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Gesture Recognition ◽  
Adaptive Learning ◽  
State Of The Art ◽  
Learning Method ◽  
Cloud Server ◽  
The Matrix ◽  
Benchmark Datasets ◽  
World Environment ◽  
Server Collaboration

Surface electromyography (sEMG) array based gesture recognition, which is widely-used, could provide natural surfaces for human-computer interaction. Currently, most existing gesture recognition methods with sEMG array only work with the fixed and pre-defined electrodes configuration. However, changes in the number of electrodes (i.e., increment or decrement) is common in real scenarios due to the variability of physiological electrodes. In this paper, we study this challenging problem and propose a random forest based ensemble learning method, namely feature incremental and decremental ensemble learning (FIDE). FIDE is able to support continuous changes in the number of electrodes by dynamically maintaining the matrix sketches of every sEMG electrode and spatial structure of sEMG array. To evaluate the performance of FIDE, we conduct extensive experiments on three benchmark datasets, including NinaPro, CSL-hdemg, and CapgMyo. Experimental results demonstrate that FIDE outperforms other state-of-the-art methods and has the potential to adapt to the evolution of electrodes in the changing environments. Moreover, based on FIDE, we implement a multi clients/server collaboration system, namely McS, to support feature adaption in real-world environment. By collecting sEMG using two clients (smartphone and personal computer) and adaptively recognizing gestures in the cloud server, FIDE significantly improves the gesture recognition accuracy in electrode increment and decrement circumstances.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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