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https://openreview.net/forum?id=SkbvRxzObB&referrer=%5Bthe%20profile%20of%20Catalin%20Ionescu%5D(%2Fprofile%3Fid%3D~Catalin_Ionescu1) Matrix Backpropagation for Deep Networks with Structured Layers | OpenReview Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well... deep networks matrix backpropagation structured layers https://www.informit.com/articles/article.aspx?p=2990401&seqNum=5 Training Deep Networks | Cost Functions | InformIT Become acquainted with two Deep Learning techniques that work in tandem to learn artificial neural network parameters. deep networks cost functions training informit https://openreview.net/forum?id=1lqOZrdXeG Direct Parameterization of Lipschitz-Bounded Deep Networks | OpenReview This paper introduces a new parameterization of deep neural networks (both fully-connected and convolutional) with guaranteed $\ell^2$ Lipschitz bounds, i.e.... deep networks direct parameterization lipschitz bounded https://openreview.net/forum?id=kmG8vRXTFv Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting | OpenReview Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various... physical models deep networks complex dynamics https://www.utwente.nl/en/eemcs/dmb/assignments/open/bachelor/trash/20200414-adversarial-noise-in-convolutional-deep-networks/ [M] Adversarial noise in convolutional deep networks | EEMCS - DMB deep networks adversarial noise eemcs dmb https://openreview.net/forum?id=PhpCHXUx9D Deep Networks as Paths on the Manifold of Neural Representations | OpenReview Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is... deep networks on the paths https://openreview.net/forum?id=wb3wxCObbRT Growing Efficient Deep Networks by Structured Continuous Sparsification | OpenReview We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity... deep networks growing efficient structured continuous https://www.commscope.com/blog/2016/making-the-transition-to-fiber-deep-networks/ Making the Transition to Fiber Deep Networks | CommScope Building a fiber network or pushing fiber deeper can be a daunting task for any service providers. Fiber optic deployments are moving toward network... making the transition fiber deep networks commscope https://www.informit.com/articles/article.aspx?p=2990401&seqNum=6 Training Deep Networks | Cost Functions | InformIT Become acquainted with two Deep Learning techniques that work in tandem to learn artificial neural network parameters. deep networks cost functions training informit https://research.google/pubs/deep-networks-with-large-output-spaces/ Deep Networks With Large Output Spaces deep networks large output spaces https://openreview.net/forum?id=3vBtKsiHEC Why Do We Need Weight Decay for Overparameterized Deep Networks? | OpenReview Weight decay is a broadly used technique for training state-of-the-art deep networks. Despite its widespread usage, its role remains poorly understood. In this... we need weight decay deep networks https://arxiv.org/abs/2008.10936v1 [2008.10936v1] Using Deep Networks for Scientific Discovery in Physiological Signals Abstract page for arXiv paper 2008.10936v1: Using Deep Networks for Scientific Discovery in Physiological Signals deep networks scientific discovery 2008 using https://www.slideserve.com/roscoe/structure-learning-with-deep-neuronal-networks PPT - Understanding Autoencoders: Deep Networks for Dimensionality Reduction PowerPoint... This workshop explores the concept of autoencoders, a class of deep neural networks designed for dimensionality reduction and structural analysis of... deep networks dimensionality reduction ppt understanding autoencoders https://openreview.net/forum?id=S1e4jkSKvB The intriguing role of module criticality in the generalization of deep networks | OpenReview We study the phenomenon that some modules of DNNs are more critical than others. Our analysis leads us to propose a complexity measure, that is able to explain... deep networks intriguing role module https://deepai.org/publication/prospects-for-analog-circuits-in-deep-networks Prospects for Analog Circuits in Deep Networks | DeepAI Jun 23, 2021 - 06/23/21 - Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. 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First we provide a neural network generalisation bound based on... deep networks fine tuning distance based regularisation https://www.amazon.science/publications/invariant-representation-learning-for-robust-deep-networks Invariant representation learning for robust deep networks - Amazon Science Deep neural networks are often brittle to superficial perturbations of their inputs; models that perform well offline on held-out data can still break under... representation learning deep networks invariant robust amazon https://www.amazon.science/publications/critical-learning-periods-for-multisensory-integration-in-deep-networks Critical learning periods for multisensory integration in deep networks - Amazon Science We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals... multisensory integration deep networks critical learning periods https://baulab.info/ David Bau: Interpretation of Deep Networks Specializing in the analysis and control of the internal computations of deep generative models for images and text, David Bau is a leading researcher in... david bau interpretation deep networks https://openreview.net/forum?id=B1EA-M-0Z Deep Neural Networks as Gaussian Processes | OpenReview We show how to make predictions using deep networks, without training deep networks. deep neural networks gaussian processes openreview https://tech.preferred.jp/en/blog/technologies-behind-distributed-deep-learning-allreduce/ Technologies behind Distributed Deep Learning: AllReduce - Preferred Networks Tech Blog Apr 10, 2026 - This post is contributed by Mr. Yuichiro Ueno, who were a Summer intern in 2017 and a part time engineer at PFN. If the mathematical expressions are not distributed deep learning preferred networks technologies behind allreduce https://proceedings.neurips.cc/paper/2019/hash/fd95ec8df5dbeea25aa8e6c808bad583-Abstract.html Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds https://openreview.net/forum?id=q8mH2d6uw2 Deep Contract Design via Discontinuous Networks | OpenReview Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper,... contract design deep via discontinuous networks https://www.mathworks.com/help/deeplearning/ref/deepnetworkdesigner-app.html?searchHighlight=Deep%20Network%20Designer%20&s_tid=srchtitle_Deep%20Network%20Designer%20_1 Deep Network Designer - Design and visualize deep learning networks - MATLAB The Deep Network Designer app lets you import, build, visualize, and edit deep learning networks. deep network learning networks designer visualize matlab https://openreview.net/forum?id=rkxcXmtUUS How well do deep neural networks trained on object recognition characterize the mouse visual... A goal-driven approach to model four mouse visual areas (V1, LM, AL, RL) based on deep neural networks trained on static object recognition does not unveil a... https://wandb.ai/generative-adversarial-networks/dcgan-tensorflow/reports/How-to-Implement-Deep-Convolutional-Generative-Adversarial-Networks-DCGAN-in-Tensorflow--VmlldzoxNzkzNDg5 How to Implement Deep Convolutional Generative Adversarial Networks (DCGAN) in Tensorflow Jan 18, 2023 - In this short tutorial, we explore how to implement Deep Convolutional Generative Adversarial Networks in Tensorflow, with a Colab to help you follow along. how to implement adversarial networks deep https://www.southampton.ac.uk/courses/2026-27/modules/mang2112 Neural Networks, Machine Learning and Deep Learning Applied to Business | MANG2112 | University of... This course focuses on neural networks, machine learning fundamentals, and deep learning techniques, covering supervised and unsupervised learning,... applied to business neural networks machine learning https://openreview.net/forum?id=F738WY1Xm4&referrer=%5Bthe%20profile%20of%20Pierre%20Marion%5D(%2Fprofile%3Fid%3D~Pierre_Marion1) Deep linear networks for regression are implicitly regularized towards flat minima | OpenReview The largest eigenvalue of the Hessian, or sharpness, of neural networks is a key quantity to understand their optimization dynamics. In this paper, we study... linear networks https://archive.org/embed/Redwood_Center_2023_06_14_Eshed_Margalit Eshed Margalit: Navigating cortical maps with topographic deep neural networks: a unifying... Talk by Eshed Margalit of Stanford University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.Abstract: Virtually all cortical... deep neural networks https://www.mdpi.com/2079-9292/10/14/1669 Deep Collaborative Learning for Randomly Wired Neural Networks A deep collaborative learning approach is introduced in which a chain of randomly wired neural networks is trained simultaneously to improve the overall... collaborative learning deep randomly wired neural https://arxiv.org/abs/2003.13746 [2003.13746] DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain... Abstract page for arXiv paper 2003.13746: DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips deep neural networks https://www.pearltrees.com/u/92673736-neural-networks-deep-learning Neural networks and deep learning | Pearltrees Note: You can find the entire source code on this GitHub repo tl;dr We will build a deep neural network that can recognize images with an accuracy of 78.4% neural networks deep learning pearltrees https://openreview.net/forum?id=SCD0hn3kMHw In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? | OpenReview We propose metrics to empirically measure invariance and equivariance and use these to answer questions about deep learning models. https://openreview.net/forum?id=BylldnNFwS On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective | OpenReview Tropical geometry can be leveraged to represent the decision boundaries of neural networks and bring to light interesting insights. deep neural networks https://arxiv.org/abs/1411.0247 [1411.0247] Random feedback weights support learning in deep neural networks Abstract page for arXiv paper 1411.0247: Random feedback weights support learning in deep neural networks support learning 1411 random feedback weights https://deepai.org/publication/unified-identification-and-tuning-approach-using-deep-neural-networks-for-visual-servoing-applications Unified Identification and Tuning Approach Using Deep Neural Networks For Visual Servoing... Jul 4, 2021 - 07/04/21 - Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability o... deep neural networks https://deepai.org/publication/rethinking-the-usage-of-batch-normalization-and-dropout-in-the-training-of-deep-neural-networks Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks |... May 15, 2019 - 05/15/19 - In this work, we propose a novel technique to boost training efficiency of a neural network. Our work is based on an excellent ide... https://deepai.org/publication/excitation-dropout-encouraging-plasticity-in-deep-neural-networks Excitation Dropout: Encouraging Plasticity in Deep Neural Networks | DeepAI May 23, 2018 - 05/23/18 - We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction: the firing of neurons in ... deep neural networks excitation dropout encouraging plasticity https://arxiv.org/abs/2505.03577 [2505.03577] Information-theoretic reduction of deep neural networks to linear models in the... Abstract page for arXiv paper 2505.03577: Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime https://openreview.net/forum?id=FX6Mei4T9K&referrer=%5Bthe%20profile%20of%20Fuyuki%20Ishikawa%5D(%2Fprofile%3Fid%3D~Fuyuki_Ishikawa2) Federated Repair of Deep Neural Networks | OpenReview As DNNs are embedded in more and more critical systems, it is essential to ensure that they perform well on specific inputs. DNN repair has shown good results... deep neural networks federated repair openreview https://aclanthology.org/2023.eacl-main.98/ Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? - ACL Anthology Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui. Proceedings of the 17th Conference of the... deep neural networks capture https://www.kth.se/math/kalender/nik-tavakolian-revealing-the-inner-workings-of-deep-neural-networks-a-graph-based-interpretability-framework-1.1373626?date=2024-12-02&orgdate=2024-05-15&length=1&orglength=0 Nik Tavakolian: Revealing the Inner Workings of Deep neural Networks: A Graph-Based... deep neural networks https://openreview.net/forum?id=wvLQMHtyLk Foiling Explanations in Deep Neural Networks | OpenReview Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. 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To gain a better understanding of how Artificial... deep neural networks https://arxiv.org/abs/2407.15236 [2407.15236] Deep State Space Recurrent Neural Networks for Time Series Forecasting Abstract page for arXiv paper 2407.15236: Deep State Space Recurrent Neural Networks for Time Series Forecasting recurrent neural networks deep state https://www.mathworks.com/help/deeplearning/export-deep-neural-networks.html Export Deep Neural Networks - MATLAB & Simulink Export networks to external deep learning platforms deep neural networks export matlab simulink https://www.shiksha.com/online-courses/deep-learning-a-z-hands-on-artificial-neural-networks-course-udeml450 Deep Learning A-Z: Hands-On Artificial Neural Networks by UDEMY : Fee, Review, Duration | Shiksha... https://www.kansascityfed.org/research/research-working-papers/macroeconomic-indicator-forecasting-deep-neural-networks-2017/ Macroeconomic Indicator Forecasting with Deep Neural Networks - Federal Reserve Bank of Kansas City New forecasting models based on deep neural networks may improve the accuracy of economic forecasts. deep neural networks federal reserve bank https://openreview.net/forum?id=DALK4KJTjX What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective |... Freezing layers in deep neural networks has been shown to enhance generalization and accelerate training, yet the underlying mechanisms remain unclear. This... deep neural networks https://arxiv.org/abs/1810.03982 [1810.03982] Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks Abstract page for arXiv paper 1810.03982: Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks https://www.uniba.it/it/ricerca/dipartimenti/informatica/notizie-eventi/eventi/poisson-sum-product-networks-a-deep-architecture-for-tractable-multivariate-poisson-distributions Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions... Seminari Prof. Kristian Kersting Dottorato di Ricerca in Informatica e Matematica deep architecture poisson sum product networks https://openreview.net/forum?id=QkRbdiiEjM AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | OpenReview The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of... graph convolutional networks deep models adaboosting openreview https://www.osti.gov/biblio/1808257 Robust training and initialization of deep neural networks: an adaptive basis viewpoint.... Abstract not provided. | OSTI.GOV deep neural networks https://www.thenationalnews.com/business/deep-in-the-ocean-internet-networks-hang-by-a-thread-1.512261 Deep in the ocean, internet networks hang by a thread | The National Jun 21, 2021 - It has been 141 years since Twenty Thousand Leagues Under the Sea was first published. But despite technological advances, humankind knows little about the... deep in the ocean internet https://arxiv.org/abs/2301.02288 [2301.02288] gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks Abstract page for arXiv paper 2301.02288: gRoMA: a Tool for Measuring the Global Robustness of Deep Neural Networks https://www.inderscience.com/info/inarticle.php?artid=96584 Article: Gain parameter and dropout-based fine tuning of deep networks Journal: International... Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science,... https://github.com/fabi92/eccv18-rgb_pose_refinement GitHub - fabi92/eccv18-rgb_pose_refinement: Inference code and trained networks for Deep... Inference code and trained networks for Deep Model-Based 6D Pose Refinement in RGB - fabi92/eccv18-rgb_pose_refinement https://openreview.net/forum?id=cnqyzuZhSo The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks |... The use of parallel actors for data collection has been an effective technique used in reinforcement learning (RL) algorithms. The manner in which data is... the impact of deep reinforcement learning https://www.datacamp.com/ru/tutorial/introduction-to-deep-neural-networks Introduction to Deep Neural Networks | DataCamp Understanding deep neural networks and their significance in the modern deep learning world of artificial intelligence deep neural networks introduction to datacamp https://www.mdpi.com/2073-8994/15/9/1723 Deep Learning and Neural Networks: Decision-Making Implications Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. 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In ECCV, 2016. - richzhang/colorization deep neural networks github colorization automatic using https://www.coursera.org/learn/deep-learning-frameworks-and-neural-networks-simplified?authMode=login Deep Learning Frameworks and Neural Networks Simplified | Coursera Offered by Simplilearn. This comprehensive Deep Learning program will equip you with advanced skills in TensorFlow, Keras, Recurrent Neural ... Enroll for free. deep learning frameworks neural networks simplified coursera https://deepai.org/publication/skin-lesion-classification-using-hybrid-deep-neural-networks Skin Lesion Classification Using Hybrid Deep Neural Networks | DeepAI Feb 27, 2017 - 02/27/17 - Skin cancer is one of the major types of cancers and its incidence has been increasing over the past decades. Skin lesions can ari... deep neural networks skin lesion classification using hybrid https://openreview.net/forum?id=Oy9WeuZD51 A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks |... Background. Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a distribution similar to that of the training set. However, DNNs'... https://arxiv.org/abs/2504.15051 [2504.15051] VeLU: Variance-enhanced Learning Unit for Deep Neural Networks Abstract page for arXiv paper 2504.15051: VeLU: Variance-enhanced Learning Unit for Deep Neural Networks enhanced learning velu variance https://www.coursera.org/learn/packt-deep-learning-convolutional-neural-networks-with-tensorflow-pjnky Deep Learning: Convolutional Neural Networks with TensorFlow | Coursera Offered by Packt. Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time ... 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Biology-inspired deep learning, where the architecture of the... deep neural networks reliable interpretability biology inspired https://deepai.org/publication/deep-back-projection-networks-for-super-resolution Deep Back-Projection Networks For Super-Resolution | DeepAI Mar 7, 2018 - 03/07/18 - The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs,... back projection super resolution deep networks https://openreview.net/forum?id=uRHpgo6TMR Sampling weights of deep neural networks | OpenReview We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a... deep neural networks sampling weights openreview https://easychair.org/publications/keyword/vMHZ Keyword: deep fusion networks deep fusion keyword networks