Deep Learning

Deep Learning news and articles

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Meta today announced the newest update of its open source machine learning framework, PyTorch. According to the company, PyTorch 2.0 is the initial step towards the next gen 2 series release of PyTorch. This release is intended to improve performance...


Keywords: framework, deep learning framework, deep
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The goal of skorch is to provide seamless integration of PyTorch with the scikit learn ecosystem. It aims to support as many features of scikit learn as possible while being able to take full advantage of PyTorch s dynamic computation graphs and auto...


Keywords: network, scikit, pytorch, neural network
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Weekly collection of the Top 20 articles. The Alarming Influence of Effective Altruism A.I. image tool produces graphic and disturbing images Apple Releases Optimizations for Core ML and Stable Diffusion AI Risky Investment The Three Different Types...


Keywords: deepfake, prototype, cryptocurrency, summarization, computer
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Source Freepik.comI used machine learning techniques to classify four different types of brain tumors No Tumor, Glioma Tumor, Meningioma Tumor, and Pituitary Tumor. So, thought of writing an article about thesame.I will divide this article into two...


Keywords: pre-trained, classification, metric, analysis, python
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Released two years ago, OpenAI 8217 s remarkably capable, if flawed, GPT 3 was perhaps the first to demonstrate that AI can write convincingly 8212 if not perfectly 8212 like human. The successor to GPT 3, most likely called GPT 4, is expected...


Keywords: analysis, openai, tpu, statistic, chatbot
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Graphs, an abstract, non linear data type, are frequently employed to illustrate links between different types of data, such as social media connections, hierarchies, financial transactions, etc. To conduct deep learning on these graphical images, re...


Keywords: sampling, network, artificial intelligence, metric
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Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as the most dominant method for image classification. However, a severe drawback of deep convolutional neural networks is poor explainability. Unfortunately, in many real-world applications, users need to understand the rationale behind the predictions of deep convolutional neural networks when determining whether they should trust the predictions or not. To resolve this issue, a novel genetic algorith...


Keywords: network, classification, neural network, genetic,
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Give rumors about GPT 4 miss. Engage with OpenAIs ChatGPT for content and coding tasks When is GPT 4 coming out While tech enthusiasts are occupied with the rumors about GPT 4,The post Are the Rumors About GPT Fake OpenAI Seems Confused appeared fi...


Keywords: chatbot, analytic, openai, reinforcement learning
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I m try to run some ML on Java 17 using Yolov5, DJL with OpenCV.I have built model and exported to torchscript. When run my code in IntelliJ all works fine, photos are processed and the object are recognized on them... great But when try to run t...


Keywords: ml , ai , yolo
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We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism in order to improve the modeling of long-term dependencies in sequential data. This model is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). By considering a suitable time-discretization scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay. We prove the existence and uniqueness o...


Keywords: ...
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In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one varia...


Keywords: deep learning, dl , transfer
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want to share that have been generating neural network using GPT over the past two days. Here are links to the Autoencoder and VAE for audio, the code that GPT generated submitted by u Nik uson link comments...


Keywords: network, gpt, neural network
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We define the bicategory of Graph Convolutional Neural Networks $\mathbf{GCNN}_n$ for an arbitrary graph with $n$ nodes. We show it can be factored through the already existing categorical constructions for deep learning called $\mathbf{Para}$ and $\mathbf{Lens}$ with the base category set to the CoKleisli category of the product comonad. We prove that there exists an injective-on-objects, faithful 2-functor $\mathbf{GCNN}_n \to \mathbf{Para}(\mathsf{CoK \times n} \times -))$. We...


Keywords: network, metric, node, deep learning,
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Data are the core of deep learning (DL), and the quality of data significantly affects the performance of DL models. However, high-quality and well-annotated databases are hard or even impossible to acquire for use in many applications, such as structural risk estimation and medical diagnosis, which is an essential barrier that blocks the applications of DL in real life. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. It can be ...


Keywords: network, dl , database, aws,
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We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on unstructured data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. In the setting of neural compression, we show that a QuadConv-based autoencoder, resulting in a Quadrature Convolutional Neural Network (QCNN), can match the performance of standard d...


Keywords: neural network, network, deep learning

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