Sensory processing and categorization in cortical and deep neural networks by Pinotsis, Dimitrios, Miller, Earl K

“…We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. …”
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Do deep neural networks suffer from crowding? by Poggio, Tomaso, Roig, Gemma

“…In this work, we study the effect of crowding in artificial Deep Neural Networks (DNNs) for object recognition. We analyze both deep convolutional neural networks (DCNNs) as well as an extension of DCNNs that are multi-scale and that change the receptive field size of the convolution filters with their position in the image. …”
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Do deep neural networks suffer from crowding? by Volokitin, Anna, Roig, Gemma, Poggio, Tomaso

“…In this work, we study the effect of crowding in artificial Deep Neural Networks (DNNs) for object recognition. We analyze both deep convolutional neural networks (DCNNs) as well as an extension of DCNNs that are multi-scale and that change the receptive field size of the convolution filters with their position in the image. …”
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Adversarial Robustness Guarantees for Random Deep Neural Networks by De Palma, Giacomo, Kiani, Bobak T, Lloyd, Seth

“…The results are based on the recently proved equivalence between Gaussian processes and deep neural networks in the limit of infinite width of the hidden layers, and are validated with experiments on both random deep neural networks and deep neural networks trained on the MNIST and CIFAR10 datasets. …”
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Deep neural networks for financial time series forecasting by Liu, Ting-Jun

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Towards deep neural networks robust to adversarial examples by Matyasko, Alexander

“…Nevertheless, state-of-the-art deep neural networks are prone to small perturbations in the input data. …”
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Review sentiment analysis based on deep neural network by Song, Wanzhen

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Embedding watermarks into deep neural networks of audio classification by Chin, Jun Ying

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Housing price prediction using deep neural networks by Yapary, Stephen

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Few-shot visual understanding with deep neural networks by Zhang, Chi

“…Deep Neural Networks (DNNs) have become indispensable for a variety of computer vision tasks, such as image recognition, image segmentation, and object detection. …”
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Using deep neural networks for chess position evaluation by Phang, Benito Yan Feng

“…This paper presents a neural network, based on Giraffe by Lai, that evaluates chess positions. It relies on deep neural networks and supervised learning. The paper aims to compare and suggest improvements to the neural network by following the architecture and feature selection of Lai’s Giraffe closely. …”
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Deep neural network with fuzzy inputs for portfolio management by Lim, Alston Khian Heng

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Deep neural networks for identifying causal relations in texts by Chen, Siyuan

“…Most of the recent approaches to this problem adopt deep neural networks to learn the inter-clause dependencies from training data, without making full use of information of all granularities and external knowledge. …”
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Evaluation of backdoor attacks and defenses to deep neural networks by Ooi, Ying Xuan

“…The proliferation of Artificial Intelligence in our daily lives has inevitably attracted the omnipresent threat of backdoor attacks in deep neural networks from adversary. This study aimed to enhance awareness on various notorious backdoor attacks and the defense practices by assessing the effectiveness, stealthiness of the attacks, and the resilience of their countermeasures. …”
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Motive imagery scoring based on deep neural network by Yu, Sicheng

“…With the evolution and development of deep learning, deep neural networks have played an important role in data processing. …”
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Target tracking using deep neural network (DNN) by Hu, Minghui

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Natural gradient algorithms for training deep neural networks by Puiu, CO

Subjects: “…Optimization for Deep Neural Networks…”

Structural test coverage criteria for deep neural networks by Sun, Y, Huang, X, Kroening, D, Sharp, J, Hill, M, Ashmore, R

“…Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. …”

Deep neural networks have an inbuilt Occam’s razor by Mingard, C, Rees, H, Valle-Pérez, G, Louis, AA

“…The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. …”

On the impact of the activation function on deep neural networks training by Hayou, S, Doucet, A, Rousseau, J

“…The weight initialization and the activation function of deep neural networks have a crucial impact on the performance of the training procedure. …”