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In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with the highest accuracy for a large range of imaging modalities and conditions. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.

More generally, it implements a way to have ""computed parameters"". In other words, after putting a parametrization `f` on `layer.weight`, `layer.weight` will return `f(weight)`. A module that implements a parametrisation may also have a `right_inverse` method. If this method is present, it is possible to assign to a parametrised tensor. This is useful when initialising a parametrised tensor. This means that we replace a parameter `weight` in a layer with `f(weight)`, where `f` is an arbitrary module. This feature allows for a simple implementation of methods like pruning, weight_normalization or spectral_normalization. This feature can be seen as a first step towards invertible modules. From this perspective, parametrisation maps an unconstrained tensor to a constrained space such as the space of orthogonal matrices, SPD matrices, low-rank matrices. In particular, it may also help making distributions first-class citizens in PyTorch. They implement a caching system, so that the value `f(weight)` is computed just once during the forward pass. Parametrisations also allows for a simple implementation of constrained optimisation. "This poster presents the ""parametrizations"" feature that will be added to PyTorch in 1.9.0. This approach is implemented in the library GeoTorch (https://github.com/Lezcano/geotorch/)."

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TorchScript removes the dependency on Python and produces portable, self contained, static representations of code and weights. With the introduction of TorchScript, PyTorch has solid tooling for addressing some of the problems of deploying PyTorch models. When deploying on NVIDIA GPUs, TensorRT, NVIDIA's deep learning optimizer, provides the capability to maximize performance of workloads by tuning the execution of models for specific target hardware. However, when moving from research to production, some of the features that make PyTorch great for development make it hard to deploy. But in addition to portability, users also look to optimize performance in deployment. We present TRTorch, a compiler for PyTorch and TorchScript targeting NVIDIA GPUs, Binary Options which combines the usability of PyTorch with the performance of TensorRT and allows users to fully optimize their inference workloads without leaving the PyTorch ecosystem. It also simplifies conducting complex optimizations like PTQ by leveraging common PyTorch tooling. For experimentation and the development of machine learning models, few tools are as approachable as PyTorch. TRTorch can be used directly from PyTorch as a TorchScript Backend, embedded in an application or used from the command line to easily increase the performance of inference applications. TensorRT also provides tooling for conducting further optimization through mixed and reduced precision execution and post training quantization (PTQ).