arXiv subjects

Yilin Yang

Publications and source records attributed to Yilin Yang.

30 records · Page 2Linked to original sources

arXiv2022

Sentiment Analysis of Covid-related Reddits

This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER and TextBlob to label messages for Machine Learning experiments. Our results show that removal of shortest and longest messages improves VADER and TextBlob agreement on positive sentiments and F-score of sentiment classification by all the three algorithms

cs.CL↗

arXiv2022

Improving Multilingual Translation by Representation and Gradient Regularization

Multilingual Neural Machine Translation (NMT) enables one model to serve all translation directions, including ones that are unseen during training, i.e. zero-shot translation. Despite being theoretically attractive, current models often produce low quality translations -- commonly failing to even produce outputs in the right target language. In this work, we observe that off-target translation is dominant even in strong multilingual systems, trained on massive multilingual corpora. To address this issue, we propose a joint approach to regularize NMT models at both representation-level and gradient-level. At the representation level, we leverage an auxiliary target language prediction task to regularize decoder outputs to retain information about the target language. At the gradient level, we leverage a small amount of direct data (in thousands of sentence pairs) to regularize model gradients. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance by +5.59 and +10.38 BLEU on WMT and OPUS datasets respectively. Moreover, experiments show that our method also works well when the small amount of direct data is not available.

cs.CL↗

arXiv2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive, and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could either do the same amount of work in less time, or more work in the same time. In this work, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band (NEB) for two reactions. In all cases the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an ASE-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

cond-mat.mtrl-sci↗

arXiv2020

On the Sub-Layer Functionalities of Transformer Decoder

There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role. During translation, the decoder must predict output tokens by considering both the source-language text from the encoder and the target-language prefix produced in previous steps. In this work, we study how Transformer-based decoders leverage information from the source and target languages -- developing a universal probe task to assess how information is propagated through each module of each decoder layer. We perform extensive experiments on three major translation datasets (WMT En-De, En-Fr, and En-Zh). Our analysis provides insight on when and where decoders leverage different sources. Based on these insights, we demonstrate that the residual feed-forward module in each Transformer decoder layer can be dropped with minimal loss of performance -- a significant reduction in computation and number of parameters, and consequently a significant boost to both training and inference speed.

cs.CL↗

arXiv2020

Towards Frequency-Based Explanation for Robust CNN

Current explanation techniques towards a transparent Convolutional Neural Network (CNN) mainly focuses on building connections between the human-understandable input features with models' prediction, overlooking an alternative representation of the input, the frequency components decomposition. In this work, we present an analysis of the connection between the distribution of frequency components in the input dataset and the reasoning process the model learns from the data. We further provide quantification analysis about the contribution of different frequency components toward the model's prediction. We show that the vulnerability of the model against tiny distortions is a result of the model is relying on the high-frequency features, the target features of the adversarial (black and white-box) attackers, to make the prediction. We further show that if the model develops stronger association between the low-frequency component with true labels, the model is more robust, which is the explanation of why adversarially trained models are more robust against tiny distortions.

cs.LG↗

arXiv2020

EchoLock: Towards Low Effort Mobile User Identification

User identification plays a pivotal role in how we interact with our mobile devices. Many existing authentication approaches require active input from the user or specialized sensing hardware, and studies on mobile device usage show significant interest in less inconvenient procedures. In this paper, we propose EchoLock, a low effort identification scheme that validates the user by sensing hand geometry via commodity microphones and speakers. These acoustic signals produce distinct structure-borne sound reflections when contacting the user's hand, which can be used to differentiate between different people based on how they hold their mobile devices. We process these reflections to derive unique acoustic features in both the time and frequency domain, which can effectively represent physiological and behavioral traits, such as hand contours, finger sizes, holding strength, and gesture. Furthermore, learning-based algorithms are developed to robustly identify the user under various environments and conditions. We conduct extensive experiments with 20 participants using different hardware setups in key use case scenarios and study various attack models to demonstrate the performance of our proposed system. Our results show that EchoLock is capable of verifying users with over 90% accuracy, without requiring any active input from the user.

cs.HC↗

arXiv2020

Numerical Methods for Biomembranes: conforming subdivision methods versus non-conforming PL methods

The Canham-Helfrich-Evans models of biomembranes consist of a family of geometric constrained variational problems. In this article, we compare two classes of numerical methods for these variational problems based on piecewise linear (PL) and subdivision surfaces (SS). Since SS methods are based on spline approximation and can be viewed as higher order versions of PL methods, one may expect that the only difference between the two methods is in the accuracy order. In this paper, we prove that a numerical method based on minimizing any one of the `PL Willmore energies' proposed in the literature would fail to converge to a solution of the continuous problem, whereas a method based on minimization of the bona fide Willmore energy, well-defined for SS but not PL surfaces, succeeds. Motivated by this analysis, we propose also a regularization method for the PL method based on techniques from conformal geometry. We address a number of implementation issues crucial for the efficiency of our solver. A software package called Wmincon accompanies this article, provides parallel implementations of all the relevant geometric functionals. When combined with a standard constrained optimization solver, the geometric variational problems can then be solved numerically. To this end, we realize that some of the available optimization algorithms/solvers are capable of preserving symmetry, while others manage to break symmetry; we explore the consequences of this observation.

math.NA↗

arXiv2019

Software Engineering Practice in the Development of Deep Learning Applications

Deep-Learning(DL) applications have been widely employed to assist in various tasks. They are constructed based on a data-driven programming paradigm that is different from conventional software applications. Given the increasing popularity and importance of DL applications, software engineering practitioners have some techniques specifically for them. However, little research is conducted to identify the challenges and lacks in practice. To fill this gap, in this paper, we surveyed 195 practitioners to understand their insight and experience in the software engineering practice of DL applications. Specifically, we asked the respondents to identify lacks and challenges in the practice of the development life cycle of DL applications. The results present 13 findings that provide us with a better understanding of software engineering practice of DL applications. Further, we distil these findings into 7 actionable recommendations for software engineering researchers and practitioners to improve the development of DL applications.

cs.SE↗

arXiv2019

Deep learning based mood tagging for Chinese song lyrics

Nowadays, listening music has been and will always be an indispensable part of our daily life. In recent years, sentiment analysis of music has been widely used in the information retrieval systems, personalized recommendation systems and so on. Due to the development of deep learning, this paper commits to find an effective approach for mood tagging of Chinese song lyrics. To achieve this goal, both machine-learning and deep-learning models have been studied and compared. Eventually, a CNN-based model with pre-trained word embedding has been demonstrated to effectively extract the distribution of emotional features of Chinese lyrics, with at least 15 percentage points higher than traditional machine-learning methods (i.e. TF-IDF+SVM and LIWC+SVM), and 7 percentage points higher than other deep-learning models (i.e. RNN, LSTM). In this paper, more than 160,000 lyrics corpus has been leveraged for pre-training word embedding for mood tagging boost.

cs.CL↗

arXiv2018

Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation

Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search. It has been widely observed that, however, beam sizes larger than 5 hurt translation quality. We explain why this happens, and propose several methods to address this problem. Furthermore, we discuss the optimal stopping criteria for these methods. Results show that our hyperparameter-free methods outperform the widely-used hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve the best results among all methods on Chinese-to-English translation.

cs.CL↗

arXiv2018

Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report

This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation. In this paper, we introduce a simple approach to incorporate image information by feeding image features to the decoder side. We also explore different sequence level training methods including scheduled sampling and reinforcement learning which lead to substantial improvements. Our systems ensemble several models using different architectures and training methods and achieve the best performance for three subtasks: En-De and En-Cs in task 1 and (En+De+Fr)-Cs task 1B.

cs.CL↗

arXiv2015

Three consecutive almost squares

Given a positive integer $n$, we let ${\rm sfp}(n)$ denote the squarefree part of $n$. We determine all positive integers $n$ for which $\max \{ {\rm sfp}(n), {\rm sfp}(n+1), {\rm sfp}(n+2) \} \leq 150$ by relating the problem to finding integral points on elliptic curves. We also prove that there are infinitely many $n$ for which \[ \max \{ {\rm sfp}(n), {\rm sfp}(n+1), {\rm sfp}(n+2) \} < n^{1/3}. \]

math.NT↗