A review of molecular representation in the age of machine learning
Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …
other things, advances in computing, machine learning, and artificial intelligence. Everyone …
Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …
the distribution of training samples. Research has fragmented into various interconnected …
Deep reinforcement learning at the edge of the statistical precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …
their relative performance on a large suite of tasks. Most published results on deep RL …
On neural differential equations
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Learning transferable visual models from natural language supervision
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …
object categories. This restricted form of supervision limits their generality and usability since …
Earthformer: Exploring space-time transformers for earth system forecasting
Conventionally, Earth system (eg, weather and climate) forecasting relies on numerical
simulation with complex physical models and hence is both expensive in computation and …
simulation with complex physical models and hence is both expensive in computation and …
Transgan: Two pure transformers can make one strong gan, and that can scale up
The recent explosive interest on transformers has suggested their potential to become
powerful``universal" models for computer vision tasks, such as classification, detection, and …
powerful``universal" models for computer vision tasks, such as classification, detection, and …
Deep discriminative transfer learning network for cross-machine fault diagnosis
Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …
alignment and knowledge transfer between the target domain and the source domain …
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
On aliased resizing and surprising subtleties in gan evaluation
Metrics for evaluating generative models aim to measure the discrepancy between real and
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …
generated images. The oftenused Frechet Inception Distance (FID) metric, for example …