Fold implementsdynamic batching. Batches of arbitrarily shaped computation graphs are transformed to produce a static computation graph. This graph has the same structure regardless of what input it receives, and can be executed efficiently by TensorFlow. ...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems). Survey Papers 2025 Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (WSDM, 2025) [paper][code] ...
Google-AI-Blog (2017) Announcing tensorflow fold: deep learning with dynamic computation graphs. Google AI Blog—the latest new from Google AI. https://ai.googleblog.com/2017/02/announcing-tensorflow-fold-deep.html. Accessed 19 Sept 2018 GoogleTPU (2018) Google announces a new generation for ...
Developed by Microsoft and Facebook, ONNX proves to be a deep learning framework that enables developers to switch easily between platforms. This deep learning framework comes with definitions on in-built operators, standard data types as well as definitions of an expandable computation graph model....
allow you to run each code line, these frameworks generate graphs in alternate layers and can be easier to debug. Such features make them essential for building and training deep learning processes and neural network models. An example of a framework that uses dynamic graphs is the PyTorch ...
there are two essential differences between supervised learning and reinforcement learning: first, there is no complete access to the function, which requires optimization, meaning that it should be queried via interaction; second, the state being interacted with is founded on an environment, where th...
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure but rather determined from the equilibrium distribution of structures. Conventional methods...
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier ...
The method includes performing decentralized distributed deep learning training on a batch of training data. Additionally, the method includes determining a training time wherein the learner performs the decentralized distributed deep learning training on the batch of training data. Further, the method ...
Deep Learning with Dynamic Computation Graphs [arXiv] Skip Connections as Effective Symmetry-Breaking [arXiv] odelSemi-Supervised QA with Generative Domain-Adaptive Nets [arXiv] 2017-01 Wasserstein GAN [arXiv] Deep Reinforcement Learning: An Overview [arXiv] DyNet: The Dynamic Neural Network Toolki...