Deep Learning



Best matching news and articles for Deep Learning


Title Summary/Keywords
Banach Space Representer Theorems for Neural Networks and Ridge Splines
http://jmlr.org/ | Yesterday
Content:
We develop variational framework to understand the properties of the functions learned by neural networks fit to data. We propose and study family of continuous domain linear inverse problems with total variation like regularization in the Radon domain subject to data fitting constraints. We derive representer theorem showing that finite width, single hidden layer neural networks are solutions to these inverse problems. We draw on many techniques from variational spline theory and so we pr...


Keywords: network, design, framework, neural network
Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures
http://article.sapub.org/ | Yesterday
Content:
Publication year 2017Source American Journal of Intelligent Systems, Volume 7, Number 5Birsen Eygi Erdoan, Erol Eriolu, Esra AkdenizSupport Vector Machines have been developed as an alternative for classification problems to the classical learning algorithms, such as Artificial Neural Network. Thanks to advantages of using kernel trick, having global optimum and simple geometric interpretation support vector machines are known as the best classifiers among others. Classical learning...


Keywords: classification, ios, metric, network, algorithms
Neural Networks | 3Blue1Brown
https://www.newworldai.com/ | Yesterday
Content:
Deep learning is part of machine learning with an algorithm inspired by the structure and function of the brain, which is called anThe post Neural Networks 3Blue1Brown appeared first on New World Artificial Intelligence....


Keywords: network, tpu, computer vision, artificial
LassoNet: A Neural Network with Feature Sparsity
http://jmlr.org/ | Yesterday
Content:
Much work has been done recently to make neural networks more interpretable, and one approach is to arrange for the network to use only subset of the available features. In linear models, Lasso or ell 1 regularized regression assigns zero weights to the most irrelevant or redundant features, and is widely used in data science. However the Lasso only applies to linear models. Here we introduce LassoNet, neural network framework with global feature selection. Our approach achieves feature...


Keywords: network, data science, regression, framework
Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
http://jmlr.org/ | Yesterday
Content:
How neural network behaves during the training over different choices of hyperparameters is an important question in the study of neural networks. In this work, inspired by the phase diagram in statistical mechanics, we draw the phase diagram for the two layer ReLU neural network at the infinite width limit for complete characterization of its dynamical regimes and their dependence on hyperparameters related to initialization. Through both experimental and theoretical approaches, we identify...


Keywords: statistic, network, nft, neural network
Large Data Analysis via Interpolation of Functions: Interpolating Polynomials vs Artificial Neural Networks
http://article.sapub.org/ | Yesterday
Content:
Publication year 2018Source American Journal of Intelligent Systems, Volume 8, Number 1Rohit RaturiIn this article we study function interpolation problem from interpolating polynomials and artificial neural networks point of view. Function interpolation plays very important role in many areas of experimental and theoretical sciences. Usual means of function interpolation are interpolation polynomials Lagrange, Newton, splines, Bezier, etc. . Here we show that specific strategy of functi...


Keywords: network, analysis, data analysis, neural
Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
http://jmlr.org/ | Yesterday
Content:
Recurrent neural networks RNNs are brain inspired models widely used in machine learning for analyzing sequential data. The present work is contribution towards deeper understanding of how RNNs process input signals using the response theory from nonequilibrium statistical mechanics. For class of continuous time stochastic RNNs SRNNs driven by an input signal, we derive Volterra type series representation for their output. This representation is interpretable and disentangles the...


Keywords: network, tpu, statistic, neural network
Simple scalable graph neural networks
https://blog.twitter.com/ | Yesterday
Content:
In this post, we describe graph neural network architecture SIGN that is of simple implementation and that works on very large graphs....


Keywords: network, graph neural networks, neural
Static Hand Gesture Recognition Using Multi-Layer Neural Network Classifier on Hybrid of Features
http://article.sapub.org/ | Yesterday
Content:
Publication year 2020Source American Journal of Intelligent Systems, Volume 10, Number 1Akintola K. G., Emmanuel J. A.Hand gesture recognition has gotten so many areas of application such as in human computer interaction, hearing impaired communication and systems control. Recognizing gestures in videos however is challenging task. Many techniques and features have been adopted in the literature but some of the methods still need to be improved upon. There are basically two types of gestur...


Keywords: classification, test, network, computer vision
/dbp1994/ Memorization in Deep Neural Networks: Does the Loss Function matter?
https://paperswithcode.com/ | Yesterday
Content:
Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Code...


Keywords: network, neural network
Regularizing Generative Adversarial Networks under Limited Data
https://research.google/ | Today
Content:
Recent years have witnessed the rapid progress of generative adversarial networks GANs . However, the success of the GAN models hinges on large amount of training data. This work proposes regularization approach for training robust GAN models on limited data. We theoretically show connection between the regularized loss and an f divergence called LeCam divergence, which we find is more......


Keywords: network
Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local anno...
https://www.docwirenews.com/ | Yesterday
Content:
Semi supervised training of deep convolutional neural networks with heterogeneous data and few local annotations An experiment on prostate histopathology image classificationDocWire News...


Keywords: neural network, classification, network
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
https://research.google/ | Today
Content:
Deriving interpretable prognostic features from deep learning based prognostic histopathology models remains challenge. In this study, we developed deep learning system DLS for predicting disease specific survival for stage II and III colorectal cancer using 3652 cases 27,300 slides . When evaluated on two validation datasets containing 1239 cases 9340 slides and 738 cases 7140......


Keywords: deep learning
Proposed Neural Network with FFT Transfer Function to Estimate Henon Dynamical Map
http://article.sapub.org/ | Yesterday
Content:
Publication year 2019Source American Journal of Intelligent Systems, Volume 9, Number 1Salah H. Abid, Saad S. Mahmood, Yaseen A. OraibiThe aim of this paper is to design feed forward artificial neural network Ann to estimate two dimensional Henon dynamical map by selecting an appropriate network, transfer function and node weights. The proposed network side by side with using Fast Fourier Transform FFT as transfer function is used. For different cases of the system, chaotic and noisy,...


Keywords: network, node, neural network, design
PyTorch Tutorial 101
https://pub.towardsai.net/ | Yesterday
Content:
PyTorch Tutorial 101...


Keywords: tutorial, pytorch, ai
Deep Learning with TensorFlow
http://learnstartup.net/ | Today
Content:
Deep Learning with TensorFlow...


Keywords: deep learning, tensorflow
/PaddlePaddle/ Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Bin...
https://paperswithcode.com/ | Today
Content:
To this end, we propose structure aware interactive graph neural network SIGN which consists of two components polar inspired graph attention layers PGAL and pairwise interactive pooling PiPool . Code...


Keywords: graph neural networks, neural network
/sh4174/ 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimen...
https://paperswithcode.com/ | Yesterday
Content:
Image synthesis via Generative Adversarial Networks GANs of three dimensional 3D medical images has great potential that can be extended to many medical applications, such as, image enhancement and disease progression modeling. Code...


Keywords: network
pytorch-aman 1.0.6
https://pypi.org/ | Today
Content:
pytorch aman 1.0.6...


Keywords: pytorch, ml
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
https://research.google/ | Yesterday
Content:
Graph neural networks GNNs have emerged as powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from leveraging program structure like control flow graphs, but they are not well suited to tasks like program execution that require far more sequential reasoning steps than number of GNN propagation steps. Recurrent......


Keywords: graph neural networks, network, neural
Determining Breast Cancer Biomarker Status and Associated Morphological Features Using Deep Learning
https://research.google/ | Yesterday
Content:
Background Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor ER PR HER2 . Though existing scoring systems are widely used and well validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker ndings can prompt repeat......


Keywords: deep learning
Adversarial Attacks For Fooling Deep Neural Networks
https://neurosys.com/ | Yesterday
Content:
Adversarial Attacks For Fooling Deep Neural Networks...


Keywords: network, neural network
A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Pas...
https://research.google/ | Yesterday
Content:
Passive acoustic monitoring is well established tool for researching the occurrence, movements, and ecology of wide variety of marine mammal species. Advances in hardware and data collection have exponentially increased the volumes of passive acoustic data collected, such that discoveries are now limited by the time required to analyze rather than collect the data. In order to address this......


Keywords: network, neural network
PyTorch vs. TensorFlow a detailed comparison
https://www.tooploox.com/ | Yesterday
Content:
PyTorch vs. TensorFlow detailed comparison...


Keywords: pytorch, tensorflow
How IBM Uses How to Combine Humans and Neural Networks to Achieve Better Language Intelligence
https://pub.towardsai.net/ | Yesterday
Content:
The human in the loop approach shows high efficiency in language models.Continue reading on Towards AI...


Keywords: network, ai , neural network
/lusinlu/ Bias Loss for Mobile Neural Networks
https://paperswithcode.com/ | Today
Content:
In compact CNNs, due to the limited number of parameters, abundant features are unlikely to be obtained, and feature diversity becomes an essential characteristic. Code...


Keywords: network, mobile, neural network
Graph Neural Networks as Neural Diffusion PDEs
https://blog.twitter.com/ | Today
Content:
In this post, we will discuss our recent work on neural graph diffusion networks....


Keywords: network, neural network, graph neural
Text Classification using Transformers in PyTorch
http://www.datatau.com/ | Yesterday
Content:
Text Classification using Transformers in PyTorch...


Keywords: classification, pytorch, transformer
/sqrhussain/ Structack: Structure-based Adversarial Attacks on Graph Neural Networks
https://paperswithcode.com/ | Today
Content:
In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes. Code...


Keywords: node, network, neural network, graph
Faster Meta Update Strategy for Noise-Robust Deep Learning
https://research.google/ | Today
Content:
It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta learning employs meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce novel Faster Meta Update Strategy FaMUS to replace the......


Keywords: network, deep learning, neural network
/ZongJ1111/ Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
https://paperswithcode.com/ | Today
Content:
Academic performance prediction aims to leverage student related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning. Code...


Keywords: network, neural network
/safi842/ Establishing process-structure linkages using Generative Adversarial Networks
https://paperswithcode.com/ | Today
Content:
The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Code...


Keywords: network
Framing RNN as a kernel method: A neural ODE approach
https://research.google/ | Yesterday
Content:
Building on the interpretation of recurrent neural network RNN as continuous time neural differential equation, we show, under appropriate conditions, that the solution of RNN can be viewed as linear function of specific feature set of the input sequence, known as the signature. This connection allows us to frame RNN as kernel method in suitable reproducing kernel Hilbert......


Keywords: network, neural network
PyTorch image classification with pre-trained networks
https://www.pyimagesearch.com/ | Today
Content:
In this tutorial, you will learn how to perform image classification with pre trained networks using PyTorch. Utilizing these networks, you can accurately classify 1,000 common object categories in only few lines of code. Todays tutorial is part four in 8230 The post PyTorch image classification with pre trained networks appeared first on PyImageSearch....


Keywords: classification, computer vision, tensorflow, network
Kings College London Accelerates Synthetic Brain 3D Image Creation Using AI Models Powered by Cambridge-1...
https://developer.nvidia.com/ | Today
Content:
Kings College London, along with partner hospitals and university collaborators, unveiled new details today about one of the first projects on Cambridge 1, the United Kingdoms most powerful supercomputer. The Synthetic Brain Project is focused on building deep learning models that can synthesize artificial 3D MRI images of human brains. These models can help scientists understand Continued...


Keywords: rust, test, supervised learning, metric
Kernels vs. Filters: Demystified
https://pub.towardsai.net/ | Yesterday
Content:
Deep LearningUnderstanding the difference once and forever.Photo by bruce mars on UnsplashFor most of us, who were once newbies in Deep Learning, trying tf.keras.layers.Conv2D for MNIST classification was fun. Convolutions are the building blocks of most algorithms in computer vision, except for some newer variants like Vision Transformers, Mixers, etc. which claim to solve image related problems without the use of convolutions. At the core of DL, lies Gradient Descent and its variants , whic...


Keywords: dl , tpu, computer vision
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
http://jmlr.org/ | Yesterday
Content:
Recent work has shown great promise in explaining neural network behavior. In particular, feature attribution methods explain the features that are important to model s prediction on given input. However, for many tasks, simply identifying significant features may be insufficient for understanding model behavior. The interactions between features within the model may better explain not only the model, but why certain features outrank others in importance. In this work, we present Integrated...


Keywords: quant, network, neural network
Meta-Learning Bidirectional Update Rules
https://research.google/ | Yesterday
Content:
In this paper, we introduce new type of generalized neural network where neurons and synapses maintain multiple states. We show that classical gradient based backpropagation in neural networks can be seen as special case of two state network where one state is used for activations and another for gradients, with update rules derived from the chain rule. In our generalized framework,......


Keywords: network, framework, neural network
Continuous Time Analysis of Momentum Methods
http://jmlr.org/ | Yesterday
Content:
Gradient descent based optimization methods underpin the parameter training of neural networks, and hence comprise significant component in the impressive test results found in number of applications. Introducing stochasticity is key to their success in practical problems, and there is some understanding of the role of stochastic gradient descent in this context. Momentum modifications of gradient descent such as Polyak s Heavy Ball method HB and Nesterov s method of accelerated gradients...


Keywords: network, analysis, algorithms, test, neural
torch-rs 0.0.2
https://pypi.org/ | Today
Content:
Released PyTorch Library for Remote Sensing View statistics for this project via or by using MIT License MIT License Python 3.7 WIP PyTorch implementation of popular datasets and models in remote sensing tasks Change Detection, Image Super Resolution, Land Cover Classification Segmentation, Image to Image Translation, etc. for various Optical Sentinel 2, Landsat, etc. and Synthetic Aperture Radar SAR Sentinel 1 sensors.The dataset is Multi image Super Resolution...


Keywords: iot, pytorch, python, visual, network
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
https://research.google/ | Yesterday
Content:
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network based methods either require exact light directions or groundtruth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption......


Keywords: network, framework, neural network, metric
Qualcomm Innovation Fellowship Europe rewards excellent young researchers in the field of AI and cybersecurity
https://www.qualcomm.com/ | Today
Content:
Each winner receives mentorship and 40,000 in research fundingJuly 26, 2021AMSTERDAM Qualcomm Technologies, Inc., announced today the winners of the 12th edition of Qualcomm Innovation Fellowship QIF Europe program Tim Georg Johann Rudner University of Oxford , James Allingham University of Cambridge , David Romero Vrije Universiteit Amsterdam and Siddharth Gupta EPF Lausanne .QIF is an annual program that focuses on recognizing, rewarding, and mentoring the most innovative engineering...


Keywords: artificial intelligence, excel, drone, foundation
Using The Diffusion Model, Google AI Is Able To Generate High Fidelity Images That Are Indistinguishable ...
https://www.marktechpost.com/ | Today
Content:
Using super resolution diffusion models, Google 8217 s latest super resolution research can generate realistic high resolution images from low resolution images, making it difficult for humans to distinguish between composite images and photos. Google uses the diffusion model to increase the resolution of photos, making it difficult for humans to differentiate between synthetic and real photos. Google researchers published 8230 The post Using The Diffusion Model, Google AI Is Able To Gene...


Keywords: neural network, machine learning, network
Understanding the Role of Training Regimes in Continual Learning
https://research.google/ | Yesterday
Content:
Catastrophic forgetting affects the training of neural networks, limiting their ability to learn multiple tasks sequentially. From the perspective of the well established plasticity stability dilemma, neural networks tend to be overly plastic, lacking the stability necessary to prevent the forgetting of previous knowledge, which means that as learning progresses, networks tend to forget......


Keywords: network, neural network
🛠 Introducing the Real World ML Section
https://thesequence.substack.com/ | Yesterday
Content:
128221 EditorialBuilding machine learning ML solutions at scale remains an unexplored territory for most companies. Most data science teams have solid ideas of managing the lifecycle of handful of ML models but how does an ML infrastructure for hundreds of thousands of models look like Even though the MLOps space has been growing at rapid pace, the architectures and best practices for applying those stacks at scale are being learned by trial and error. In the current ML market, some of...


Keywords: computer vision, analytic, linux, security
Kernel Operations on the GPU, with Autodiff, without Memory Overflows
http://jmlr.org/ | Yesterday
Content:
The KeOps library provides fast and memory efficient GPU support for tensors whose entries are given by mathematical formula, such as kernel and distance matrices. KeOps alleviates the main bottleneck of tensor centric libraries for kernel and geometric applications memory consumption. It also supports automatic differentiation and outperforms standard GPU baselines, including PyTorch CUDA tensors or the Halide and TVM libraries. KeOps combines optimized C CUDA schemes with binders for hi...


Keywords: pytorch, tutorial, metric, gpu, python
Document grounded generation
https://aihub.org/ | Today
Content:
Figure 1 Document Grounded Generation An example of conversation that is grounded in the given document text in green shows information from the document that was used to generate the response . Natural language generation NLG systems are increasingly expected to be naturalistic, content rich, and situation aware due to their popularity and pervasiveness in human life. This is particularly relevant in dialogue systems, machine translation systems, story generation, and question answering...


Keywords: natural language generation, tpu, nlp
nbdev 1.1.16
https://pypi.org/ | Today
Content:
Released Writing library entirely in notebooks View statistics for this project via or by using Apache Software License Apache Software License 2.0 Python 3.6 Create delightful python projects using Jupyter Notebooks is library that allows you to develop python library in putting all your code, tests and documentation in one place. That is you now have true environment, as envisioned by Donald Knuth back in 1983 makes debugging and refactoring your code much easi...


Keywords: machine learning, ai , python
Self-Distillation Amplifies Regularization in Hilbert Space
https://research.google/ | Yesterday
Content:
Knowledge distillation introduced in the deep learning context is method to transfer knowledge from one architecture to another. In particular, when the architectures are identical, this is called self distillation. The idea is to feed in predictions of the trained model as new target values for retraining and iterate this loop possibly few times . It has been empirically observed that the......


Keywords: deep learning
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
https://research.google/ | Yesterday
Content:
We describe sequence to sequence neural network which can directly generate speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating normalizing flow in the decoder loop. Output waveforms are modeled as sequence of non overlapping fixed length frames, each one containing hundreds of samples. The inter dependencies of waveform samples within each......


Keywords: network, tpu, neural network
Title
Banach Space Representer Theorems for Neural Networks and Ridge Splines
http://jmlr.org/ | Yesterday   

Keywords: network, design, framework, neural network   

Support Vector Machines vs Multiplicative Neuron Model Neural Network in Prediction of Bank Failures
http://article.sapub.org/ | Yesterday   

Keywords: classification, ios, metric, network, algorithms   

Neural Networks | 3Blue1Brown
https://www.newworldai.com/ | Yesterday   

Keywords: network, tpu, computer vision, artificial   

LassoNet: A Neural Network with Feature Sparsity
http://jmlr.org/ | Yesterday   

Keywords: network, data science, regression, framework   

Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit
http://jmlr.org/ | Yesterday   

Keywords: statistic, network, nft, neural network   

Large Data Analysis via Interpolation of Functions: Interpolating Polynomials vs Artificial Neural Networks
http://article.sapub.org/ | Yesterday   

Keywords: network, analysis, data analysis, neural   

Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory
http://jmlr.org/ | Yesterday   

Keywords: network, tpu, statistic, neural network   

Simple scalable graph neural networks
https://blog.twitter.com/ | Yesterday   

Keywords: network, graph neural networks, neural   

Static Hand Gesture Recognition Using Multi-Layer Neural Network Classifier on Hybrid of Features
http://article.sapub.org/ | Yesterday   

Keywords: classification, test, network, computer vision   

/dbp1994/ Memorization in Deep Neural Networks: Does the Loss Function matter?
https://paperswithcode.com/ | Yesterday   

Keywords: network, neural network   

Regularizing Generative Adversarial Networks under Limited Data
https://research.google/ | Today   

Keywords: network   

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local anno...
https://www.docwirenews.com/ | Yesterday   

Keywords: neural network, classification, network   

Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
https://research.google/ | Today   

Keywords: deep learning   

Proposed Neural Network with FFT Transfer Function to Estimate Henon Dynamical Map
http://article.sapub.org/ | Yesterday   

Keywords: network, node, neural network, design   

PyTorch Tutorial 101
https://pub.towardsai.net/ | Yesterday   

Keywords: tutorial, pytorch, ai   

Deep Learning with TensorFlow
http://learnstartup.net/ | Today   

Keywords: deep learning, tensorflow   

/PaddlePaddle/ Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Bin...
https://paperswithcode.com/ | Today   

Keywords: graph neural networks, neural network   

/sh4174/ 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimen...
https://paperswithcode.com/ | Yesterday   

Keywords: network   

pytorch-aman 1.0.6
https://pypi.org/ | Today   

Keywords: pytorch, ml   

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
https://research.google/ | Yesterday   

Keywords: graph neural networks, network, neural   

Determining Breast Cancer Biomarker Status and Associated Morphological Features Using Deep Learning
https://research.google/ | Yesterday   

Keywords: deep learning   

Adversarial Attacks For Fooling Deep Neural Networks
https://neurosys.com/ | Yesterday   

Keywords: network, neural network   

A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Pas...
https://research.google/ | Yesterday   

Keywords: network, neural network   

PyTorch vs. TensorFlow a detailed comparison
https://www.tooploox.com/ | Yesterday   

Keywords: pytorch, tensorflow   

How IBM Uses How to Combine Humans and Neural Networks to Achieve Better Language Intelligence
https://pub.towardsai.net/ | Yesterday   

Keywords: network, ai , neural network   

/lusinlu/ Bias Loss for Mobile Neural Networks
https://paperswithcode.com/ | Today   

Keywords: network, mobile, neural network   

Graph Neural Networks as Neural Diffusion PDEs
https://blog.twitter.com/ | Today   

Keywords: network, neural network, graph neural   

Text Classification using Transformers in PyTorch
http://www.datatau.com/ | Yesterday   

Keywords: classification, pytorch, transformer   

/sqrhussain/ Structack: Structure-based Adversarial Attacks on Graph Neural Networks
https://paperswithcode.com/ | Today   

Keywords: node, network, neural network, graph   

Faster Meta Update Strategy for Noise-Robust Deep Learning
https://research.google/ | Today   

Keywords: network, deep learning, neural network   

/ZongJ1111/ Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
https://paperswithcode.com/ | Today   

Keywords: network, neural network   

/safi842/ Establishing process-structure linkages using Generative Adversarial Networks
https://paperswithcode.com/ | Today   

Keywords: network   

Framing RNN as a kernel method: A neural ODE approach
https://research.google/ | Yesterday   

Keywords: network, neural network   

PyTorch image classification with pre-trained networks
https://www.pyimagesearch.com/ | Today   

Keywords: classification, computer vision, tensorflow, network   

Kings College London Accelerates Synthetic Brain 3D Image Creation Using AI Models Powered by Cambridge-1...
https://developer.nvidia.com/ | Today   

Keywords: rust, test, supervised learning, metric   

Kernels vs. Filters: Demystified
https://pub.towardsai.net/ | Yesterday   

Keywords: dl , tpu, computer vision   

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
http://jmlr.org/ | Yesterday   

Keywords: quant, network, neural network   

Meta-Learning Bidirectional Update Rules
https://research.google/ | Yesterday   

Keywords: network, framework, neural network   

Continuous Time Analysis of Momentum Methods
http://jmlr.org/ | Yesterday   

Keywords: network, analysis, algorithms, test, neural   

torch-rs 0.0.2
https://pypi.org/ | Today   

Keywords: iot, pytorch, python, visual, network   

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
https://research.google/ | Yesterday   

Keywords: network, framework, neural network, metric   

Qualcomm Innovation Fellowship Europe rewards excellent young researchers in the field of AI and cybersecurity
https://www.qualcomm.com/ | Today   

Keywords: artificial intelligence, excel, drone, foundation   

Using The Diffusion Model, Google AI Is Able To Generate High Fidelity Images That Are Indistinguishable ...
https://www.marktechpost.com/ | Today   

Keywords: neural network, machine learning, network   

Understanding the Role of Training Regimes in Continual Learning
https://research.google/ | Yesterday   

Keywords: network, neural network   

🛠 Introducing the Real World ML Section
https://thesequence.substack.com/ | Yesterday   

Keywords: computer vision, analytic, linux, security   

Kernel Operations on the GPU, with Autodiff, without Memory Overflows
http://jmlr.org/ | Yesterday   

Keywords: pytorch, tutorial, metric, gpu, python   

Document grounded generation
https://aihub.org/ | Today   

Keywords: natural language generation, tpu, nlp   

nbdev 1.1.16
https://pypi.org/ | Today   

Keywords: machine learning, ai , python   

Self-Distillation Amplifies Regularization in Hilbert Space
https://research.google/ | Yesterday   

Keywords: deep learning   

Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
https://research.google/ | Yesterday   

Keywords: network, tpu, neural network   






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