AI Technology & Industry Review
A research team from Rikkyo University and AnyTech Co., Ltd. examines the suitability of different inductive biases for computer vision and proposes Sequencer, an architectural alternative to ViTs that leverages long short-term memory (LSTM) rather than self-attention layers to achieve ViT-competitive performance on long sequence modelling.
DeepMind trained and tested its neural model by first collecting a dataset consisting of different types of mathematics problems. Rather than crowd-sourcing, they synthesized the dataset to generate a larger number of training examples, control the difficulty level and reduce training time.
A hearty round of applause arose from the crowd packing the Vancouver Rogers Centre on August 22 when a team of unassuming scientists wearing “OpenAI” T-shirts climbed up on stage.
Researchers from the British AI company yesterday published a paper on Nature — Vector-based navigation using grid-like representations in artificial agents — which proposes an artificial virtual agent that can navigate like mammals.
The Montreal Institute for Learning Algorithms (MILA) recently launched ObamaNet — a photo-realistic lip-sync neural network that can make anyone appear to be saying anything.
To boost learning research aimed at endowing robots with better generalization capabilities, Yi Wu from UC Berkeley and Yuxin Wu, Georgia Gkioxari, and Yuandong Tian from Facebook AI research recently published the paper Building Generalizable Agents with a Realistic and Rich 3D Environment.
To boost CNN performance, industry leaders and startups alike are now racing to develop specialized AI chips.
Much like that epoch-making event 28 years ago, AI technology is breaking down limitations and opening up new opportunities.
This paper gives a demonstration of using Bayesian LSTMs for classification of medical time series, which can improve the accuracy compared with standard LSTMs.
Attention is simply a vector, often the outputs of dense layer using softmax function.
In this paper, the author aims to design a smart way to handle rare/unknown words in the bilingual training data during machine translation.
In this paper, we present a survey on language models, which mainly consists of count-based and continuous-space language models.
This paper proposes not only a method as a useful tool but also a new concept for modeling complex annotation as a simple polygon.
Compared to SMT, NMT can train multiple features jointly and does not need prior domain knowledge, enabling zero-shot translation. In addition to higher BLEU score and better sentence structure, NMT can also help reduce morphology errors, syntax errors, and word order errors of SMT.
We interviewed Mr. Qiang Dong, chief scientist of Beijing YuZhi Language Understanding Technology Co. Ltd. and learned more about their YuZhi NLU Platform that conducts its unique semantic analysis based on concepts rather than words.
Andrew Ng, one of the top minds in deep learning, loves teaching. And so, the Stanford University professor decided toContinue Reading
The paper proposes a hybrid neural machine translation model concerning both words and characters, which mainly contributes to the translation of rare or unknown words.
We explore top-notch Swiss AI facilities: starting with deep learning and neural network research at IDSIA in Lugano, to interdisciplinary research at École Polytechnique Fédérale de Lausanne and University of Basel, and ending with robotics innovations at ETH in Zurich and University of Zurich.
By accurately predicting the category of an email, we can better extract useful information from it according to its corresponding templates.
Although no innovations for the NMT architecture was introduced, the authors claim that a classic NMT baseline system with carefully tuned hyperparameters can still achieve comparable result to the state-of-the-art.
From LSTM deep learning technique to self-learning humanoid robot, Jürgen Schmidhuber’s research teams have won awards in various machine learning competitions, including medical image recognition.
The rubber on GelSight sensor will conform to any object pressed against it, resulting in the reflective lights on the metallic paint to vary according to different materials and shapes.
This repository did some toy experiments based on Tensorflow in order to introduce some deep learning concepts which are used for image recognition and language modeling.
Andrew McCallum explained how the use of Universal schema can improve knowledge representation and reasoning from natural language.
A glance at the state of the art research shows that neural networks would still serve us, and artificial general intelligence is not yet in sight.
Lukasz Kaiser, Senior Research Scientist at Google Brain, gives a presentation about the developments in Natural Language Processing techniques at 2017’s AI Frontier Conference.