• VAMPnets

  • Referenced in 19 articles [sw32927]
  • VAMPnets: Deep learning of molecular kinetics. There is an increasing demand for computing the relevant ... Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks...
  • NETT

  • Referenced in 18 articles [sw41773]
  • well understood. Recently, novel algorithms using deep learning and neural networks for inverse problems appeared ... there are few theoretical results for deep learning in inverse problems. In this paper...
  • BinaryConnect

  • Referenced in 24 articles [sw35871]
  • development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained...
  • Xception

  • Referenced in 23 articles [sw39068]
  • Xception: Deep Learning with Depthwise Separable Convolutions. We present an interpretation of Inception modules ... observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception...
  • Edward

  • Referenced in 16 articles [sw21517]
  • models on small data sets to complex deep probabilistic models on large data sets. Edward ... three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming...
  • OverFeat

  • Referenced in 22 articles [sw17857]
  • ConvNet. We also introduce a novel deep learning approach to localization by learning to predict...
  • Chainer

  • Referenced in 15 articles [sw26707]
  • next-generation open source framework for deep learning. Chainer is a Python-based deep learning...
  • Cityscapes

  • Referenced in 21 articles [sw36624]
  • datasets, especially in the context of deep learning. For semantic urban scene understanding, however...
  • Hyperband

  • Referenced in 20 articles [sw41120]
  • approach to hyperparameter optimization. Performance of machine learning algorithms depends critically on identifying a good ... competitor set on a variety of deep-learning and kernel-based learning problems...
  • DeepStack

  • Referenced in 18 articles [sw27097]
  • automatically learned from self-play using deep learning. In a study involving 44,000 hands...
  • NICE

  • Referenced in 18 articles [sw29631]
  • Independent Components Estimation. We propose a deep learning framework for modeling complex high-dimensional densities ... trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition ... simple building blocks, each based on a deep neural network. The training criterion is simply...
  • Inception-v4

  • Referenced in 29 articles [sw39592]
  • Impact of Residual Connections on Learning. Very deep convolutional networks have been central...
  • GPT-3

  • Referenced in 18 articles [sw42135]
  • autoregressive language model that uses deep learning to produce human-like text...
  • DeepONet

  • Referenced in 50 articles [sw42093]
  • potential application of neural networks in learning nonlinear operators from data. However, the theorem guarantees ... practice, we propose deep operator networks (DeepONets) to learn operators accurately and efficiently from...
  • OctNet

  • Referenced in 11 articles [sw36665]
  • OctNet: Learning Deep 3D Representations at High Resolutions. We present OctNet, a representation for deep ... learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional ... networks which are both deep and high resolution. Towards this goal, we exploit the sparsity...
  • Wasserstein GAN

  • Referenced in 159 articles [sw42583]
  • problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches ... provide extensive theoretical work highlighting the deep...
  • Pyro

  • Referenced in 15 articles [sw27079]
  • modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with...
  • AllenNLP

  • Referenced in 14 articles [sw26526]
  • easy to design and evaluate new deep learning models for nearly any NLP problem, along...
  • DGL

  • Referenced in 11 articles [sw33907]
  • research in the emerging field of deep graph learning requires new tools to support tensor ... present the design principles and implementation of Deep Graph Library (DGL). DGL distills the computational ... leverage the existing components across multiple deep learning frameworks. Our evaluation shows that DGL significantly...
  • Dopamine

  • Referenced in 8 articles [sw31151]
  • Research Framework for Deep Reinforcement Learning. Deep reinforcement learning (deep RL) research has grown significantly...