Big RNNs Achieve SOTA Performance in Video Prediction
It’s not as easy as one might imagine to train an AI model to accurately predict what a human will do next, even when they are interacting with a relatively simple object like a ball.
Tag: RNN
ICLR 2019 | MILA, Microsoft, and MIT Share Best Paper Honours
The Seventh International Conference on Learning Representations (ICLR) kicked off today. One of the world’s major machine learning conferences, ICLR this year received 1591 main conference paper submissions — up 60 percent over last year — and accepted 24 for oral presentations and 476 as poster presentations.
MXNet Now Supports Keras 2
According to an AWS blog post, Apache MXNet now supports Keras 2 and the Keras-MXNet deep learning backend is now available to developers.
New DeepMind AI Learns to Navigate Like an Animal; Scientists React
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.
Cambricon Unveils its First AI Chip for Cloud Computing
Cambricon today unveiled its Cambricon 1M chip for edge computing, and the MLU100, the first in a new chip series for cloud computing.
Makers Put Their Specialized AI Chips on the Table
To boost CNN performance, industry leaders and startups alike are now racing to develop specialized AI chips.
Berlin: Breaking the Wall Between Human and Artificial Intelligence
Much like that epoch-making event 28 years ago, AI technology is breaking down limitations and opening up new opportunities.
Bayesian LSTMs in Medicine
This paper gives a demonstration of using Bayesian LSTMs for classification of medical time series, which can improve the accuracy compared with standard LSTMs.
AlphaGo Zero: Approaching Perfection
Facebook AI Researcher Yuandong Tian says new AlphaGo Zero paper is destined to be a classic.
A Brief Overview of Attention Mechanism
Attention is simply a vector, often the outputs of dense layer using softmax function.
Memory, Attention, Sequences
In the article “Memory, attention, sequences”, the author predicts that future work on neural networks will emphasize understanding complex spatio-temporal data from the real world, which is highly contextual and noisy.
CVPR2017 Honorable Mention Paper – Annotating Object Instances with a Polygon-RNN
This paper proposes not only a method as a useful tool but also a new concept for modeling complex annotation as a simple polygon.
History and Frontier of the Neural Machine Translation
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.
30 Years of Devotion to Natural Language Processing Based on Concepts
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.
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
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.
Massive Exploration of Neural Machine Translation Architectures
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.
Tensorflow Programs and Tutorials
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.
Exploring Sparsity in Recurrent Neural Networks
In order to deploy Recurrent Neural Networks (RNNs) efficiently, we propose a technique to reduce the parameters of a network by pruning weights during the initial training of the network.
Representations for Language: From Word Embeddings to Sentence Meanings
This talk was recently given by Prof. Christopher Manning at Simons Institute, UC Berkeley. It is an introductory tutorial without complicated algorithms.
Let Neural Networks to Memorize and Communicate
The team of Rob Fergus, who is currently a research scientist at Facebook AI Research, have devised two neural net models for handling unstructured data.
Beyond Seq2Seq with Augmented RNNs by Edward Grefenstette, Google, Inc.
This talk summarizes the limitations of RNN (including LSTM, GRU), from both empirical and computational hierarchy mechanism perspectives.