Найденные страницы с тегом pytorch всего 5826

Как я могу получить данные для колчана в факеле? – 1 Ответ

Вы можете вычислить градиенты torch.Tensor путем прохождения torch.Tensor единиц. import... Вопрос по теме: python, pytorch.

NUBIA: NeUral Based Interchangeability Assessor for Text Generation | Papers With Code

1 code implementation in PyTorch. We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA which outperforms metrics currently used to evaluate machine translation, summaries and slightly exceeds/matches state of the art metrics on correlation with human judgement on t

LiDAR guided Small obstacle Segmentation | Papers With Code

2 code implementations in PyTorch. Detecting small obstacles on the road is critical for autonomous driving. In this paper, we present a method to reliably detect such obstacles through a multi-modal framework of sparse LiDAR(VLP-16) and Monocular vision. LiDAR is employed to provide additional context in the form of confidence maps to monocular segmentation networks. We show significant performance gains when the context is fed as an additional input to monocular semantic segmentation frameworks. We furth

Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation | Papers With Code

1 code implementation in PyTorch. We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross

PyTorch 2.0 release accelerates open-source machine learning | VentureBeat

The new PyTorch 2.0 promises to accelerate ML training and development, while being backward-compatible with existing PyTorch code.

PyTorch 2.0 release accelerates open-source machine learning | VentureBeat

The new PyTorch 2.0 promises to accelerate ML training and development, while being backward-compatible with existing PyTorch code.

Getting Started with PyTorch Lightning - KDnuggets

Introduction to PyTorch Lightning and how it can be used for the model building process. It also provides a brief overview of the PyTorch characteristics and how they are different from TensorFlow.

Элементарный слой фильтра PyTorch – 1 Ответ

В pytorch вы всегда можете реализовать свои собственные слои, сделав их подклассами nn.Module. Вы... Вопрос по теме: python, python-3.x, neural-network, pytorch.

Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach | Papers With Code

1 code implementation in PyTorch. In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensit

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior | Papers With Code

1 code implementation in PyTorch. We present a simple and effective deep convolutional neural network (CNN) model for video deblurring. The proposed algorithm mainly consists of optical flow estimation from intermediate latent frames and latent frame restoration steps. It first develops a deep CNN model to estimate optical flow from intermediate latent frames and then restores the latent frames based on the estimated optical flow. To better explore the temporal information from videos, we develop a tempora

Найти глобальный минимум с GD в PyTorch – 1 Ответ

Вы делаете то, что вы сделали для обучения своей сети, но вместо обновления весов вы обновляете... Вопрос по теме: python, pytorch, regression, gradient-descent, non-linear-regression.

Check Out TorchOpt: An Efficient Library For Differentiable Optimization Built Upon PyTorch - MarkTechPost

Check Out TorchOpt: An Efficient Library For Differentiable Optimization Built Upon PyTorch

IBM Research helps extend PyTorch to enable open-source cloud-native machine learning | VentureBeat

IBM Research has contributed code to the open-source PyTorch machine learning project that could help to significantly accelerate training.

KDnuggets Top Posts for October 2022: 10 Cheat Sheets You Need To Ace Data Science Interview - KDnuggets

10 Cheat Sheets You Need To Ace Data Science Interview • 7 Free Platforms for Building a Strong Data Science Portfolio • The Complete Free PyTorch Course for Deep Learning • 3 Valuable Skills That Have Doubled My Income as a Data Scientist • 25 Advanced SQL Interview Questions for…

Point2Mesh: A Self-Prior for Deformable Meshes | Papers With Code

2 code implementations in TensorFlow and PyTorch. In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape properties, the prior is defined automatically using the input point cloud, which we refer to as a self-prior. The self-prior encapsulates reoccurring geometric repetitions from a single shape within the weights of a deep neural network. We optimize the network weights to de

Cotatron: Transcription-Guided Speech Encoder for Any-to-Many Voice Conversion without Parallel Data | Papers With Code

2 code implementations in PyTorch. We propose Cotatron, a transcription-guided speech encoder for speaker-independent linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be trained with conventional TTS datasets. We train a voice conversion system to reconstruct speech with Cotatron features, which is similar to the previous methods based on Phonetic Posteriorgram (PPG). By training and evaluating our system with 108 speakers from the VCTK dataset, we outperform the pr

Продвинутое использование библиотеки PYTORCH: от подготовки данных до визуализации / Хабр

PyTorch — современная библиотека машинного обучения с открытым исходным кодом, разработанная компанией Facebook. Как и другие популярные библиотеки, такие как TensorFlow и Keras, PyTorch позволяет...

Разбираемся с устройством свёрток на примере объединения двух свёрток в одну в pytorch / Хабр

Неинтересная цель этой статьи — показать, как можно смержить две свертки пайторча в одну. Если интересна лишь реализация — прошу в конец статьи. А интересная цель — потыкать непосредственно в веса...

Pytorch сохранение и загрузка VGG16 с передачей знаний – 1 Ответ

Почему бы не переопределить модель VGG16 напрямую? просмотреть vgg.py для подробностей class... Вопрос по теме: python, pytorch, vgg-net.

Telegram бот с языковой моделью, обученной на 2ch / Хабр

Если вам хочется разбавить общение в telegram чате нелепыми, но зачастую меткими и смешными комментариями, или вы ищете информацию по интеграции языковой модели в бота, или хотите сами обучить...

ML для оптимизации цен на основе эластичности по цене / Хабр

Статья подготовлена для конференции Aha'22 и рассказывает про задачу выставления оптимальных цен. Я в последнее время работал над этой задачей в Яндекс Маркете и попробовал выписать ряд вещей,...

Релиз языка программирования NewLang 0.3 / Хабр

NewLang — это язык программирования высокого уровня общего назначения. Основной особенностью языка является простой, логичный и не противоречивый синтаксис, который основан на строгой системе...

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework | Papers With Code

1 code implementation in PyTorch. We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MeshfreeFlowNet accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) trainin

AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing | Papers With Code

1 code implementation in PyTorch. Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods. In this paper, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather