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International Joint Conference on Neural Networks (IJCNN)

作者： Pramanik, Sayantan Chandra, M. Girish TCS Res Pune Maharashtra India

ISBN: (纸本)9780738133669

Generative Models have always attracted the attention of Machine Learning research community;they are useful and also generally harder than their discriminative counterparts. In these models, we would be looking into learning the probability distribution of the input and sampling from that to generate new data samples. Since quantum computing and algorithms are inherently random, they can facilitate a natural framework in this situation. But, getting a suitable gate circuit to achieve the requisite quantum state which by repeated preparation and measurement leads to the sought-after data samples is not trivial. In this paper, we propose a quantum circuit which has a flavor of variational autoencoder with the usual visible and hidden nodes for input data and latent distribution. The encoder portion comprises of a suitably chosen parameterized phase ansatz and Inverse Quantum Fourier Transform blocks. Depending on whether the measurement is carried out on the hidden nodes or not, the decoder circuit, which is just not the inverse of the encoder in our case, is configured. The Kullback-Leibler Divergence is used train the circuit towards the required input distribution. Numerical results presented demonstrate the correct functionality of the approach.

关键词： generative model variational autoencoder quantum circuit inverse quantum fourier transform ansatz kullback-liebler divergence

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A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a variational autoencoder 17

A supervised subtype differentiation learning for building i...

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17th International Symposium on Medical Information Processing and Analysis

作者： Cano, Fabian Cruz-Roa, Angel Alvarez-Jimenez, Charlens Becerra, David Siabatto, Andres Romero, Eduardo Univ Nacl Colombia Sede Bogota Comp Imaging & Med Applicat Lab CIMA LAB Bogota Colombia Univ Llanos GITECX Res Grp Automat Data Driven Analyt Lab AdaLab Villavicencio Meta Colombia

ISBN: (数字)9781510650534

ISBN: (纸本)9781510650534;9781510650527

This work presents a supervised subtype differentiation learning of lung cancer features in a latent space constructed with a variational autoencoder. In such space, complicated patterns are quantified by estimating a differentiation grade of typical encoded features of lung cancer subtypes. Specifically, selected tissue samples of non-small cell lung cancer are mapped to a latent space and a logistic regression model assigns differentiation cancer subtype grade to the embedded tissue samples. The latent representation captures the invariant features of the most representative tissue samples for both well-differentiated adenocarcinoma and squamous cell, and confusing cases of poorly differentiated complex mixtures of tissue patterns subtypes. This approach builds up a subtype differentiation grade of non-small cell lung cancer among complex structures which are fully interpretable and integrable with a pathology workflow. Typical tissue samples of well-differentiated lung cancer subtypes are grouped close in the latent space with high confidence of the differentiation grade, while poorly differentiated tissue samples, with lower confidence of the differentiation grade, are located at other latent space regions. A variational autoencoder (VAE) was trained to learn the latent space representation with training data of representative tissue samples picked from well-differentiated adenocarcinoma (five cases) and squamous cell (five cases) lung cancer subtypes. Validation was performed by selecting six cases for training and evaluating the location in the latent space of tissue samples from four different cases. Two different metrics, MAE and RMSE, estimated the location of these patches with respect to the patches belonging to the six cases. The best model, under a cross validation, achieves an average performance of MAE = (0.072 +/- 0.0004) and RMSE = (0.2654 +/- 0.0019). In addition, for ten different cases (five adenocarcinoma and five of squamous cell), performance

关键词： Lung Cancer Latent Space variational autoencoder Metric Learning Digital Pathology

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Learning robust speech representation with an articulatory-regularized variational autoencoder 22

Learning robust speech representation with an articulatory-r...

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Interspeech Conference

作者： Georges, Marc-Antoine Girin, Laurent Schwartz, Jean-Luc Hueber, Thomas Univ Grenoble Alpes CNRS GIPSA Lab F-38000 Grenoble France Univ Grenoble Alpes CNRS LPNC F-38000 Grenoble France

ISBN: (纸本)9781713836902

It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representation could improve the performances of a deep generative model (here a variational autoencoder) trained to encode and decode acoustic speech features. First we develop an articulatory model able to associate articulatory parameters describing the jaw, tongue, lips and velum configurations with vocal tract shapes and spectral features. Then we incorporate these articulatory parameters into a variational autoencoder applied on spectral features by using a regularization technique that constrains part of the latent space to represent articulatory trajectories. We show that this articulatory constraint improves model training by decreasing time to convergence and reconstruction loss at convergence, and yields better performance in a speech denoising task.

关键词： Speech production representation learning variational autoencoder articulatory model speech enhancement

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GUIDED variational autoencoder FOR SPEECH ENHANCEMENT WITH A SUPERVISED CLASSIFIER

GUIDED VARIATIONAL AUTOENCODER FOR SPEECH ENHANCEMENT WITH A...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Carbajal, Guillaume Richter, Julius Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany

ISBN: (纸本)9781728176055

Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal from noisy speech compared to supervised approaches. In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech. The estimated label is a high-level categorical variable describing the speech signal (e.g. speech activity) allowing for a more informed latent distribution compared to the standard variational autoencoder. We evaluate our method with different types of labels on real recordings of different noisy environments. Provided that the label better informs the latent distribution and that the classifier achieves good performance, the proposed approach outperforms the standard variational autoencoder and a conventional neural network-based supervised approach.

关键词： Speech enhancement deep generative model variational autoencoder semi-supervised learning

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Behavioral Cloning in Atari Games Using a Combined variational autoencoder and Predictor Model

Behavioral Cloning in Atari Games Using a Combined Variation...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chen, Brian Tandon, Siddhant Gorsich, David Gorodetsky, Alex Veerapaneni, Shravan Univ Michigan Dept Math Ann Arbor MI 48109 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA US Army DEVCOM Ground Vehicle Syst Ctr Warren MI USA

ISBN: (纸本)9781728183923

We explore an approach to behavioral cloning in video games. We are motivated to pursue a learning architecture that is data efficient and provides opportunity for interpreting player strategies and replicating player actions in unseen situations. To this end, we have developed a generative model that learns latent features of a game that can be used for training an action predictor. Specifically, our architecture combines a variational autoencoder with a discriminator mapping the latent space to action predictions (predictor). We compare our model performance to two different behavior cloning architectures: a discriminative model (a Convolutional Neural Network) mapping game states directly to actions, and a variational autoencoder with a predictor trained separately. Finally, we demonstrate how we can use the advantage of generative modeling to sample new states from the latent space of the variational autoencoder to analyze player actions and provide meaning to certain latent features.

关键词： Behavior Cloning variational autoencoder Predictive Models Video Games

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CLASSIFICATION OF EXPERT-NOVICE LEVEL USING EYE TRACKING AND MOTION DATA VIA CONDITIONAL MULTIMODAL variational autoencoder

CLASSIFICATION OF EXPERT-NOVICE LEVEL USING EYE TRACKING AND...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Akamatsu, Yusuke Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Off Inst Res Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781728176055

Sensor data from wearable devices have been utilized to analyze differences between experts and novices. Previous studies attempted to classify the expert-novice level from sensor data based on supervised learning methods. However, these approaches need to collect enough training data covering various novices' sensor patterns. In this paper, we propose a semi-supervised anomaly detection approach that requires only sensor data of experts for training and identifies those of novices as anomalies. Our proposed anomaly detection model named conditional multimodal variational autoencoder (CMVAE) has the following two technical contributions: (i) considering action information of persons and (ii) utilizing multimodal sensor data, i.e., eye tracking data and motion data in this case. The proposed method is evaluated on sensor data measured when expert and novice soccer players were shooting, dribbling, and doing soccer ball juggling. Experimental results show that CMVAE can more accurately classify the expert-novice level than previous supervised learning methods and anomaly detection methods using other VAEs.

关键词： Wearable devices sensor data expert-novice level anomaly detection variational autoencoder

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A QUATERNION-VALUED variational autoencoder

A QUATERNION-VALUED VARIATIONAL AUTOENCODER

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Grassucci, Eleonora Comminiello, Danilo Uncini, Aurelio Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781728176055

Deep probabilistic generative models have achieved incredible success in many fields of application. Among such models, variational autoencoders (VAEs) have proved their ability in modeling a generative process by learning a latent representation of the input. In this paper, we propose a novel VAE defined in the quaternion domain, which exploits the properties of quaternion algebra to improve performance while significantly reducing the number of parameters required by the network. The success of the proposed quaternion VAE with respect to traditional VAEs relies on the ability to leverage the internal relations between quaternion-valued input features and on the properties of second-order statistics which allow to define the latent variables in the augmented quaternion domain. In order to show the advantages due to such properties, we define a plain convolutional VAE in the quaternion domain and we evaluate its performance with respect to its real-valued counterpart on the CelebA face dataset.

关键词： variational autoencoder Quaternion Neural Networks Quaternion Properness Generative Models Quaternion Random Vectors

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variational autoencoder for Image-Based Augmentation of Eye-Tracking Data

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JOURNAL OF IMAGING 2021年第5期7卷 83-83页

作者： Elbattah, Mahmoud Loughnane, Colm Guerin, Jean-Luc Carette, Romuald Cilia, Federica Dequen, Gilles Univ Picardie Jules Verne Lab Modelisat Informat Syst MIS F-80080 Amiens France Univ Limerick Fac Sci & Engn Limerick V94 T9PX Ireland Evolucare Technol F-80800 Villers Bretonneux France Univ Picardie Jules Verne Lab CRP CPO F-80000 Amiens France

Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives. Such challenges have attached significance to the application of generative modeling and data augmentation in that domain. In this context, this study explores a machine learning-based approach for generating synthetic eye-tracking data. We explore a novel application of variational autoencoders (VAEs) in this regard. More specifically, a VAE model is trained to generate an image-based representation of the eye-tracking output, so-called scanpaths. Overall, our results validate that the VAE model could generate a plausible output from a limited dataset. Finally, it is empirically demonstrated that such approach could be employed as a mechanism for data augmentation to improve the performance in classification tasks.

关键词： deep learning variational autoencoder data augmentation eye-tracking

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Text Generation with Syntax-Enhanced variational autoencoder

Text Generation with Syntax-Enhanced Variational Autoencoder

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuan, Weijie Ding, Linyi Meng, Kui Liu, Gongshen Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9780738133669

Text generation is one of the essential yet challenging tasks in natural language processing. However, the input text alone is usually hard to provide enough information to generate the desired output. Previous work attempts to incorporate syntactic information into the generative models based on variational autoencoder(VAE). But these methods have difficulty in adequately modeling the tree structure of syntactic data. In this paper, we formulate the syntactic structure as a graph and introduce a syntax encoder based on graph neural network(GNN) to model the syntactic information of sentences. Based on the syntax encoder, we propose a novel syntax-enhanced variational autoencoder(SEVAE) with two variants. The variant SEVAEm merges sentence information and syntactic information into one latent space to enrich the fine-grained syntactic information of latent representations. And the variant SEVAE-s with two separate latent spaces allows the sentence decoder to dynamically attend to semantic and syntactic information from two latent variables. Experiments on two benchmark datasets show that our methods achieve significant and consistent improvements compared with previous work.

关键词： text generation variational autoencoder syntactic modeling attention mechanism

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Whisper Speech Enhancement Using Joint variational autoencoder for Improved Speech Recognition 22

Whisper Speech Enhancement Using Joint Variational Autoencod...

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Interspeech Conference

作者： Agrawal, Vikas Kumar, Shashi Rath, Shakti P. Samsung R&D Inst India Bangalore Karnataka India Reverie Language Technol Bangalore Karnataka India

ISBN: (纸本)9781713836902

Whispering is the natural choice of communication when one wants to interact quietly and privately. Due to vast differences in acoustic characteristics of whisper and natural speech, there is drastic degradation in the performance of whisper speech when decoded by the Automatic Speech Recognition (ASR) system trained on neutral speech. Recently, to handle this mismatched train and test scenario Denoising autoencoders (DA) are used which gives some improvement. To improve over DA performance we propose another method to map speech from whisper domain to neutral speech domain via Joint variational Auto-Encoder (JVAE). The proposed method requires time-aligned parallel data which is not available, so we developed an algorithm to convert parallel data to time-aligned parallel data. JVAE jointly learns the characteristics of whisper and neutral speech in a common latent space which significantly improves whisper recognition accuracy and outperforms traditional autoencoder based techniques. We benchmarked our method against two baselines, first being ASR trained on neutral speech and tested on whisper dataset and second being whisper test set mapped using DA and tested on same neutral ASR. We achieved an absolute improvement of 22.31% in Word Error Rate (WER) over the first baseline and an absolute 5.52% improvement over DA.

关键词： whisper speech recognition autoencoder wTIMIT variational autoencoder jointVAE

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