Top PDF Toward robust deep neural networks
Toward robust deep neural networks
... that are semantically and statistically different from those in-distribution ones. For instance, a real-world image-based digit reader model may be exposed to an enormous types of images that do not contain any digit, in ...
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Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications
... synthesizer. Toward this goal, a prerequisite is the development a synthesizer that should i) produce intelligible speech, ii) run in real time, iii) depend on as few parameters as possible, and iv) be ...
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Automated Seismic Source Characterization Using Deep Graph Neural Networks
... of deep neural networks suitable for time series analysis, the recurrent neural networks (RNN; Hochreiter & Schmidhuber, 1997; Sherstinsky, 2020), allows for online (real-time) ...
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Toward a Deep Neural Approach for Knowledge-Based IR
... of deep learn- ing in ad-hoc IR tasks as well as the representation learning approach of words surrounded by external ...within deep structure neural net- ...other deep neural network ...
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Comparing learned representations of deep neural networks
... to utilize different non-robust features, which can serve as a defense against transfer attacks. Another key insight from these experiments is that certain network architectures consistently learn more similar ...
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Deep neural networks for choice analysis
... highly robust regardless of the values of the other four hyperparameters: in all four figures, the dashed green curves are always placed higher than the dashed red curves ...
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Impact of reverberation through deep neural networks on adversarial perturbations
... defenses, learning-based adversarial detection methods uti- lize adversarial examples during the training phase, but of the model used as a detector (e.g. [ 10 ]). Although both pop- ular and reliable, these methods ...
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Emotion Recognition with Deep Neural Networks
... be robust to noisy ...Multimodal Deep Learning Approaches for Emotion Recogni- tion in Video” (Kahou et ...presents deep learning approaches for emotion recognition from video clips and provides an ...
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Robust parallel-gripper grasp getection using convolutional neural networks
... in deep computer ...the deep learning age, it is now common to learn in a single model Fea- ture extraction and Heuristic grasp generation (Joseph Redmon and Angelova, 2015 ; Trottier, Giguère, and ...
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2020 — Modeling information flow through deep convolutional neural networks
... optimizing deep convolutional neural networks (CNN) by 1) reducing the computational complexity and 2) improving classification performance for the task of transfer ...
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Deep Background Subtraction with Scene-Specific Convolutional Neural Networks
... IV. C ONCLUSION In this paper, we present a novel background subtraction algorithm based on convolutional neural networks (ConvNets). Rather than building a sophisticated background model to deal with ...
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On the Expressive Power of Deep Fully Circulant Neural Networks
... compact neural networks: deep networks in which all weight matrices are either diag- onal or circulant ...such networks with a large number of layers had not been done ...that ...
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Robust detection of astronomical sources using convolutional neural networks
... Received 18 July 2019 / Accepted 9 December 2019 ABSTRACT In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are ...
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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... ORK Deep model for computer vision and natural ...the deep neural network developed rapidly in recent years in both the field of computer vision and natural lan- ...a deep Convolutional ...
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Unsupervised Layer-Wise Model Selection in Deep Neural Networks
... standard Neural Nets, where the com- plexity of the model is dominated by the mere size of the weight vec- ...that deep networks actually depend on the sequential acquisition of different “skills”, ...
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Clinical event prediction and understanding with deep neural networks
... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ...
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Adaptive structured noise injection for shallow and deep neural networks
... 1 Introduction The tremendous empirical success of deep neural networks (DNN) for many machine learning tasks such as image classification and object recognition (Krizhevsky et al., 2017) contrasts ...
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Deep neural networks for automatic classification of anesthetic-induced unconsciousness
... with deep neural networks to automatically discriminate anesthetic states induced by ...tional neural networks significantly outperform multilayer perceptrons in dis- crimination ...
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Deep neural networks for natural language processing and its acceleration
... of neural networks approach has reunited them under the name of artificial intelligence in this recent ...this neural network approach has become the dominant method in the machine learning ...
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Classification of Time-Series Images Using Deep Convolutional Neural Networks
... Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw ...the deep CNN ...existing ...
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