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"speech": models, code, and papers

Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

Jan 05, 2022
Subhabrata Dutta, Samiya Caur, Soumen Chakrabarti, Tanmoy Chakraborty

Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of social media text and issues on which users opine. Homophily properties over the social network provide strong signal of coarse-grained user-level stance. But semi-supervised approaches for tweet-level stance detection fail to properly leverage homophily. In light of this, We present SANDS, a new semi-supervised stance detector. SANDS starts from very few labeled tweets. It builds multiple deep feature views of tweets. It also uses a distant supervision signal from the social network to provide a surrogate loss signal to the component learners. We prepare two new tweet datasets comprising over 236,000 politically tinted tweets from two demographics (US and India) posted by over 87,000 users, their follower-followee graph, and over 8,000 tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49) on US (India)-based datasets, outperforming 17 baselines (including variants of SANDS) substantially, particularly for minority stance labels and noisy text. Numerous ablation experiments on SANDS disentangle the dynamics of textual and network-propagated stance signals.

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Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors

Apr 19, 2021
Lukas Baischer, Matthias Wess, Nima TaheriNejad

Deep neural networks (DNNs) have the advantage that they can take into account a large number of parameters, which enables them to solve complex tasks. In computer vision and speech recognition, they have a better accuracy than common algorithms, and in some tasks, they boast an even higher accuracy than human experts. With the progress of DNNs in recent years, many other fields of application such as diagnosis of diseases and autonomous driving are taking advantage of them. The trend at DNNs is clear: The network size is growing exponentially, which leads to an exponential increase in computational effort and required memory size. For this reason, optimized hardware accelerators are used to increase the performance of the inference of neuronal networks. However, there are various neural network hardware accelerator platforms, such as graphics processing units (GPUs), application specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs). Each of these platforms offer certain advantages and disadvantages. Also, there are various methods for reducing the computational effort of DNNs, which are differently suitable for each hardware accelerator. In this article an overview of existing neural network hardware accelerators and acceleration methods is given. Their strengths and weaknesses are shown and a recommendation of suitable applications is given. In particular, we focus on acceleration of the inference of convolutional neural networks (CNNs) used for image recognition tasks. Given that there exist many different hardware architectures. FPGA-based implementations are well-suited to show the effect of DNN optimization methods on accuracy and throughput. For this reason, the focus of this work is more on FPGA-based implementations.

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Optimising AI Training Deployments using Graph Compilers and Containers

Sep 17, 2020
Nina Mujkanovic, Karthee Sivalingam, Alfio Lazzaro

Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing(HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads. AI training deployments in HPC or cloud can be optimised with target-specific libraries, graph compilers, andby improving data movement or IO. Graph compilers aim to optimise the execution of a DNN graph by generating an optimised code for a target hardware/backend. As part of SODALITE (a Horizon 2020 project), MODAK tool is developed to optimise application deployment in software defined infrastructures. Using input from the data scientist and performance modelling, MODAK maps optimal application parameters to a target infrastructure and builds an optimised container. In this paper, we introduce MODAK and review container technologies and graph compilers for AI. We illustrate optimisation of AI training deployments using graph compilers and Singularity containers. Evaluation using MNIST-CNN and ResNet50 training workloads shows that custom built optimised containers outperform the official images from DockerHub. We also found that the performance of graph compilers depends on the target hardware and the complexity of the neural network.

* HPEC IEEE, 6 pages, 5 figues, 1 table 

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Evaluating the Communication Efficiency in Federated Learning Algorithms

Apr 06, 2020
Muhammad Asad, Ahmed Moustafa, Takayuki Ito, Muhammad Aslam

In the era of advanced technologies, mobile devices are equipped with computing and sensing capabilities that gather excessive amounts of data. These amounts of data are suitable for training different learning models. Cooperated with advancements in Deep Learning (DL), these learning models empower numerous useful applications, e.g., image processing, speech recognition, healthcare, vehicular network and many more. Traditionally, Machine Learning (ML) approaches require data to be centralised in cloud-based data-centres. However, this data is often large in quantity and privacy-sensitive which prevents logging into these data-centres for training the learning models. In turn, this results in critical issues of high latency and communication inefficiency. Recently, in light of new privacy legislations in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models, without sharing the privacy-sensitive data. Usually, these mobile users have slow network connections to the data-centre where the global model is maintained. Moreover, in a complex and large scale network, heterogeneous devices that have various energy constraints are involved. This raises the challenge of communication cost when implementing FL at large scale. To this end, in this research, we begin with the fundamentals of FL, and then, we highlight the recent FL algorithms and evaluate their communication efficiency with detailed comparisons. Furthermore, we propose a set of solutions to alleviate the existing FL problems both from communication perspective and privacy perspective.

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LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge

Dec 19, 2019
Xiaoxiao Miao, Ian McLoughlin

This paper presents a novel Dialect Identification (DID) system developed for the Fifth Edition of the Multi-Genre Broadcast challenge, the task of Fine-grained Arabic Dialect Identification (MGB-5 ADI Challenge). The system improves upon traditional DNN x-vector performance by employing a Convolutional and Long Short Term Memory-Recurrent (CLSTM) architecture to combine the benefits of a convolutional neural network front-end for feature extraction and a back-end recurrent neural to capture longer temporal dependencies. Furthermore we investigate intensive augmentation of one low resource dialect in the highly unbalanced training set using time-scale modification (TSM). This converts an utterance to several time-stretched or time-compressed versions, subsequently used to train the CLSTM system without using any other corpus. In this paper, we also investigate speech augmentation using MUSAN and the RIR datasets to increase the quantity and diversity of the existing training data in the normal way. Results show firstly that the CLSTM architecture outperforms a traditional DNN x-vector implementation. Secondly, adopting TSM-based speed perturbation yields a small performance improvement for the unbalanced data, finally that traditional data augmentation techniques yield further benefit, in line with evidence from related speaker and language recognition tasks. Our system achieved 2nd place ranking out of 15 entries in the MGB-5 ADI challenge, presented at ASRU 2019.

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Utterance-Based Audio Sentiment Analysis Learned by a Parallel Combination of CNN and LSTM

Nov 20, 2018
Ziqian Luo, Hua Xu, Feiyang Chen

Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from speech. However, current progress on audio sentiment analysis mainly focuses on extracting homogeneous acoustic features or doesn't fuse heterogeneous features effectively. In this paper, we propose an utterance-based deep neural network model, which has a parallel combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based network, to obtain representative features termed Audio Sentiment Vector (ASV), that can maximally reflect sentiment information in an audio. Specifically, our model is trained by utterance-level labels and ASV can be extracted and fused creatively from two branches. In the CNN model branch, spectrum graphs produced by signals are fed as inputs while in the LSTM model branch, inputs include spectral features and cepstrum coefficient extracted from dependent utterances in an audio. Besides, Bidirectional Long Short-Term Memory (BiLSTM) with attention mechanism is used for feature fusion. Extensive experiments have been conducted to show our model can recognize audio sentiment precisely and quickly, and demonstrate our ASV are better than traditional acoustic features or vectors extracted from other deep learning models. Furthermore, experimental results indicate that the proposed model outperforms the state-of-the-art approach by 9.33% on Multimodal Opinion-level Sentiment Intensity dataset (MOSI) dataset.

* 15 pages, 3 figures, journal 

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Kernel Density Estimation-Based Markov Models with Hidden State

Jul 30, 2018
Gustav Eje Henter, Arne Leijon, W. Bastiaan Kleijn

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic representations of stationary processes, based on techniques with strong asymptotic consistency properties. The models generate new data by concatenating points from the training data sequences in a context-sensitive manner, together with some additive driving noise. We present novel EM-type maximum-likelihood algorithms for data-driven bandwidth selection in KDE-MMs. Additionally, we augment the KDE-MMs with a hidden state, yielding a new model class, KDE-HMMs. The added state variable captures non-Markovian long memory and signal structure (e.g., slow oscillations), complementing the short-range dependences described by the Markov process. The resulting joint Markov and hidden-Markov structure is appealing for modelling complex real-world processes such as speech signals. We present guaranteed-ascent EM-update equations for model parameters in the case of Gaussian kernels, as well as relaxed update formulas that greatly accelerate training in practice. Experiments demonstrate increased held-out set probability for KDE-HMMs on several challenging natural and synthetic data series, compared to traditional techniques such as autoregressive models, HMMs, and their combinations.

* 14 pages, 6 figures 

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Distributed Hessian-Free Optimization for Deep Neural Network

Jan 15, 2017
Xi He, Dheevatsa Mudigere, Mikhail Smelyanskiy, Martin Takáč

Training deep neural network is a high dimensional and a highly non-convex optimization problem. Stochastic gradient descent (SGD) algorithm and it's variations are the current state-of-the-art solvers for this task. However, due to non-covexity nature of the problem, it was observed that SGD slows down near saddle point. Recent empirical work claim that by detecting and escaping saddle point efficiently, it's more likely to improve training performance. With this objective, we revisit Hessian-free optimization method for deep networks. We also develop its distributed variant and demonstrate superior scaling potential to SGD, which allows more efficiently utilizing larger computing resources thus enabling large models and faster time to obtain desired solution. Furthermore, unlike truncated Newton method (Marten's HF) that ignores negative curvature information by using na\"ive conjugate gradient method and Gauss-Newton Hessian approximation information - we propose a novel algorithm to explore negative curvature direction by solving the sub-problem with stabilized bi-conjugate method involving possible indefinite stochastic Hessian information. We show that these techniques accelerate the training process for both the standard MNIST dataset and also the TIMIT speech recognition problem, demonstrating robust performance with upto an order of magnitude larger batch sizes. This increased scaling potential is illustrated with near linear speed-up on upto 16 CPU nodes for a simple 4-layer network.

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