/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Social Network Analysis

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Convex Analysis: (pms-28)

Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of fully end-to-end inference. Experimental results show that 1) FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s enjoys even faster inference speed; 2) FastSpeech 2 and 2s outperform FastSpeech in voice quality, and FastSpeech 2 can even surpass autoregressive models. Audio samples are available at this https URL.

FastSpeech 2: Fast and High-Quality End-to-End Text to Speech

User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem --- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4186&context=sis_research

User Identity Linkage by Latent User Space Modelling

Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel. AlignTTS is based on a Feed-Forward Transformer which generates mel-spectrum from a sequence of characters, and the duration of each character is determined by a duration predictor. Instead of adopting the attention mechanism in Transformer TTS to align text to mel-spectrum, the alignment loss is presented to consider all possible alignments in training by use of dynamic programming. Experiments on the LJSpeech dataset show that our model achieves not only state-of-the-art performance which outperforms Transformer TTS by 0.03 in mean option score (MOS), but also a high efficiency which is more than 50 times faster than real-time.

Aligntts: Efficient Feed-Forward Text-to-Speech System Without Explicit Alignment

Federated learning has sparkled new interests in the deep learning society to make use of isolated data sources from independent institutes. With the development of novel training tools, we have successfully deployed federated natural language processing networks on GPU-enabled server clusters. This paper demonstrates federated training of a popular NLP model, TextCNN, with applications in sentence intent classification. Furthermore, differential privacy is introduced to protect participants in the training process, in a manageable manner. Distinguished from previous client-level privacy protection schemes, the proposed differentially private federated learning procedure is defined in the dataset sample level, inherent with the applications among institutions instead of individual users. Optimal settings of hyper-parameters for the federated TextCNN model are studied through comprehensive experiments. We also evaluated the performance of federated TextCNN model under imbalanced data load configuration. Experiments show that, the sampling ratio has a large impact on the performance of the FL models, causing up to 38.4% decrease in the test accuracy, while they are robust to different noise multiplier levels, with less than 3% variance in the test accuracy. It is also found that the FL models are sensitive to data load balancedness among client datasets. When the data load is imbalanced, model performance dropped by up to 10%.

/pdf/empirical-studies-of-institutional-federated-learning-for-q9v1g3ru3f.pdf

Empirical Studies of Institutional Federated Learning For Natural Language Processing

Unsegmented text recognition is a crucial component in financial document processing systems. Financial text materials, such as receipts, transcripts, identification documents, etc. often involve critical personal information. In many circumstances, these data reside in protected servers of different institutions and must not be transferred beyond the institutional firewall. The emerging technology of Federated Learning (FL) provides a data-secure way of uniting isolated datasets in model training. Using the FL framework, text recognition models can be trained with larger collection of image samples. In previous works, federated text recognition models only deal with single-character images and alpha-numeric corpus. Such models are not competent in industrial applications, especially in Chinese text recognition problems. In this paper, we apply federated learning with a deep convolutional network to perform variable-length text string recognition with a large corpus. In our experiments, we compared two prevalent federated learning frameworks, namely, Tensorflow Federated and PySyft. Results show that federated text recognition models can achieve similar or even higher accuracy than models trained on deep learning framework. On a 5-client distributed dataset, the best character accuracy is achieved by TFF at 49.20%. Extensive experiments are also conducted to evaluate the effect of distributed data storage over the performance of trained models. TFF again achieved a maximum character precision of 54.33% with non-distributed dataset.

Federated Learning of Unsegmented Chinese Text Recognition Model

In federated learning (FL), fair and accurate measurement of the contribution of each federated participant is of great significance. The level of contribution not only provides a rational metric for distributing financial benefits among federated participants, but also helps to discover malicious participants that try to poison the FL framework. Previous methods for contribution measurement were based on enumeration over possible combination of federated participants. Their computation costs increase drastically with the number of participants or feature dimensions, making them inapplicable in practical situations. In this paper an efficient method is proposed to evaluate the contributions of federated participants. This paper focuses on the horizontal FL framework, where client servers calculate parameter gradients over their local data, and upload the gradients to the central server. Before aggregating the client gradients, the central server train a data value estimator of the gradients using reinforcement learning techniques. As shown by experimental results, the proposed method consistently outperforms the conventional leave-one-out method in terms of valuation authenticity as well as time complexity.

Xiao Jing

Papers

User Identity Linkage by Latent User Space Modelling

Aligntts: Efficient Feed-Forward Text-to-Speech System Without Explicit Alignment

Empirical Studies of Institutional Federated Learning For Natural Language Processing

Federated Learning of Unsegmented Chinese Text Recognition Model

Efficient Client Contribution Evaluation for Horizontal Federated Learning