AI Technology & Industry Review
A research team from Microsoft, Zhejiang University, Johns Hopkins University, Georgia Institute of Technology and University of Denver proposes Only-Train-Once (OTO), a one-shot DNN training and pruning framework that produces a slim architecture from a full heavy model without fine-tuning while maintaining high performance.
A research team from OneFlow and Microsoft takes a step toward automatic deep neural network structure design, exploring unsupervised structure-learning and leveraging the efficient coding principle, information theory and computational neuroscience to design structure learning without label information.
A team from Cornell University and NTT Research proposes Physical Neural Networks (PNNs), a universal framework that leverages a backpropagation algorithm to train arbitrary, real physical systems to execute deep neural networks.
University of Toronto researchers propose a BERT-inspired training approach as a self-supervised pretraining step to enable deep neural networks to leverage newly and publicly available massive EEG (electroencephalography) datasets for downstream brain-computer-interface (BCI) applications.
DeepMind has designed a family of Normalizer-Free ResNets (NFNets) that can be trained in larger batch sizes and stronger data augmentations and have set new SOTA validation accuracies on ImageNet.
Synced invited Dr. Linchao Zhu, a lecturer at the ReLER lab, University of Technology Sydney whose works focus on video representation learning, to share his thoughts on the paper Text-to-Image Generation Grounded by Fine-Grained User Attention.
Facebook AI says DNNs can perform well without class specific neurons and overreliance on intuition-based methods for understanding DNNs can be misleading.
The trimmed-down pQRNN extension to Google AI’s projection attention neural network PRADO compares to BERT on text classification tasks for on-device use.
Researchers unify DNN normalization layers and activation functions into a single computation graph.
Researchers proposed an automatic structured pruning framework, AutoCompress, which adopts the 2018 ADMM-based weight pruning algorithm and outperforms previous automatic model compression methods while maintaining high accuracy.
A new study suggests DeepMind’s amazing game-playing algorithm AlphaZero could help unlock the power and potential of quantum computing.
A team of researchers from Mila and Google Brain believe simple pencil sketches could help AI models generalize to a better understanding of unseen images.
Recognizing faces is as natural and habitual as can be for human beings. But what actually happens inside our brains during the process of recognizing a face? Can artificial intelligence learn to recognize faces the way humans do?
More and more social apps are now leveraging AI technologies such as machine learning, deep neural networks and facial recognition to enable users to create highly-individualized emojis from selfies and photos.
Thanks to the creation of AutoML — which is essentially automated neural architecture search (NAS) — AI can now design better deep neural networks than human researchers for computer vision tasks such as image classification and object detection.
The Neural Architects Workshop gathers experts and researchers in the field of deep neural network (DNN) design to share their insights and experiences working in this domain.
Microsoft has updated the performance of their Multi-Task Deep Neural Network (MT-DNN) ensemble model. The significant performance boost has the model sitting comfortably atop the benchmark GLUE rankings, as shown in the following table.
Deep learning model performance has taken huge strides, allowing researchers to tackle tasks which were simply not possible for machines less than a decade ago.
A group of researchers from Tencent Technology, the Chinese University of Hong Kong, and Nankai University recently combined two commonly used techniques — Batch Normalization (BatchNorm) and Dropout — into an Independent Component (IC) layer inserted before each weight layer to make inputs more independent*.
Google AI yesterday released its latest research result in speech-to-speech translation, the futuristic-sounding “Translatotron.”
A collaboration between researchers from China’s Beihang University and Microsoft Research Asia has produced TableBank, a new image-based dataset for table detection and recognition built with novel weak supervision from Word and Latex documents on the Internet.
Microsoft researchers have released technical details of an AI system that combines both approaches. The new Multi-Task Deep Neural Network (MT-DNN) is a natural language processing (NLP) model that outperforms Google BERT in nine of eleven benchmark NLP tasks.
New research from Carnegie Mellon University, Peking University and the Massachusetts Institute of Technology shows that global minima of deep neural networks can been achieved via gradient descent under certain conditions. The paper Gradient Descent Finds Global Minima of Deep Neural Networks was published November 12 on arXiv.
The reason why TensorFlow is so widely used is due to its automatic derivation of functions and distributed computing capability. This increase in interest can also be found in commercial applications.
A practical way to make an autonomous vehicle is not by programming a car to drive in any environment, but by showing the car how to drive and make the car learn by itself. NVIDIA created a system of this kind, named PilotNet.
Andrew McCallum explained how the use of Universal schema can improve knowledge representation and reasoning from natural language.
Neuromorphic computing, a promising direction for future of computing that emulates human brain to maintain energy efficiency and fast computing speed.
At RE•WORK Summits, speakers are invited to present advances from the world’s leading innovators, showcase the opportunities in emerging health care industry
This talk describes the dialog system architecture and explains the three main steps of the architecture: understanding, generation, and dialog manager and their challenges for machine learning.
There are three ways to combine DL and RL, based on three different principles: value-based, policy-based, and model-based approaches with planning.
This review will go over some of the current methods that are used to visualize and understand deep neural networks.