KaoKore: New Facial Dataset from Japanese Scrolls for ML
The KaoKore dataset includes 5552 RGB image files drawn from the 2018 Collection of Facial Expressions dataset of cropped face images from Japanese artworks.
AI Technology & Industry Review
The KaoKore dataset includes 5552 RGB image files drawn from the 2018 Collection of Facial Expressions dataset of cropped face images from Japanese artworks.
Researchers propose a novel model compression approach to effectively compress BERT by progressive module replacing.
The crowdsourcing produced 111.25 hours of video from 54 non-expert demonstrators to build “one of the largest, richest, and most diverse robot manipulation datasets ever collected using human creativity and dexterity.”
A Google-led research team has introduced a new method for optimizing neural network parameters that is faster than all common first-order methods on complex problems.
Fast and accurate diagnosis is critical on the front line, and now an AI-powered diagnostic assessment system is helping Hubei medical teams do just that.
In an attempt to equip the TF-IDF-based retriever with a state-of-the-art neural reading comprehension model, researchers introduced a new graph-based recurrent retrieval approach.
The proposed system is capable of searching the continental United States at 1 -meter pixel resolution, corresponding to approximately 2 billion images, in around 0.1 seconds.
MonoLayout, a practical deep neural architecture that takes just a single image of a road scene as input and outputs an amodal scene layout in bird’s-eye view.
In a bid to raise awareness of the threats posed by climate change, the Mila team recently published a paper that uses GANs to generate images of how climate events may impact our environments — with a particular focus on floods.
Researchers have proposed a novel self-adversarial learning (SAL) paradigm for improving GANs’ performance in text generation.
Bayesian inference meanwhile leverages Bayes’ theorem to update the probability of a hypothesis as additional data becomes available. How can Bayesian inference benefit deep learning models?
DeepMind announced yesterday the release of Haiku and RLax — new JAX libraries designed for neural networks and reinforcement learning respectively.
Researchers from Italy’s University of Pisa present a clear and engaging tutorial on the main concepts and building blocks involved in neural architectures for graphs.
Researchers have proposed a novel generator network specialized on the illustrations in children’s books.
Researchers have proposed a simple but powerful “SimCLR” framework for contrastive learning of visual representations.
A recent Google Brain paper looks into Google’s hugely successful transformer network — BERT — and how it represents linguistic information internally.
The tool enables researchers to try, compare, and evaluate models to decide which work best on their datasets or for their research purposes.
Google teamed up with researchers from Synthesis AI and Columbia University to introduce a deep learning approach called ClearGrasp as a first step to teaching machines how to “see” transparent materials.
Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.
Researchers have introduced the first unsupervised learning approach for identifying interpretable semantic directions in the latent space of generative adversarial network (GAN) models.
Deep learning models are getting larger and larger to meet the demand for better and better performance. Meanwhile, the timeContinue Reading
Researchers introduced semantic region-adaptive normalization (SEAN), a simple but effective building block for conditional Generative Adversarial Networks (cGAN).
The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).
The crucial step now is to develop matching vaccines and drugs to uproot its existence, and China’s big tech companies have stepped up to help.
Batchboost is a simple technique to accelerate ML model training by adaptively feeding mini-batches with artificial samples which are created by mixing two examples from the previous step – in favor of pairing those that produce the difficult one.
Leading scientific publication Nature announced it is launching a trial starting this week that will give authors of newly published papers the option of appending contents of the discussions they’ve had with and reports they’ve received from their reviewers.
In an effort to enrich resources for multispeaker singing-voice synthesis, a team of researchers from the University of Tokyo has developed a Japanese multispeaker singing-voice corpus.
Researchers proposed a “radioactive data” technique for subtly marking images in a dataset to help researchers later determine whether they were used to train a particular model.
In a new paper, researchers from the University of Toronto, Vector Institute, and University of Wisconsin-Madison propose SISA training, a new framework that helps models “unlearn” information by reducing number of updates that need to be computed when data points are removed.
In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, translation and cropping could help increase model generality.
The tool can significantly accelerate the prediction time of a virus’s RNA secondary structure, affording frontline researchers an opportunity to better understand the virus and develop targeting vaccines in a time of crisis.
Facebook’s new HiPlot is a lightweight interactive visualization tool that takes this further, using parallel plots to discover correlations and patterns in such high-dimensional data.
Now, DeepMind and University College London (UCL) have introduced a new deep network called MEMO which matches SOTA results on Facebook’s bAbI dataset for testing text understanding and reasoning, and is the first and only architecture capable of solving long sequence novel reasoning tasks.
A new study suggests human-to-human transmission of the 2019 Novel Coronavirus (2019-nCoV) may have started as early as mid December, 2019.
One of a new breed of open-domain chatbots designed to engage in conversations across any topic, Meena’s free and natural conversational abilities are closing the gap on human performance.
Facebook AI researchers have further developed the BART model with the introduction of mBART.
A team of researchers from the Natural Language Processing Lab at the University of British Columbia in Canada have proposed AraNet, a deep learning toolkit designed for Arabic social media processing.
Inspired by the performance of attention mechanisms in NLP, researchers have explored the possibility of applying them to vision tasks.
A new study suggests DeepMind’s amazing game-playing algorithm AlphaZero could help unlock the power and potential of quantum computing.
A recent paper published by Microsoft researchers proposes a new vision-language pretrained model for image-text joint embedding, ImageBERT, which which achieves SOTA performance on both the MSCOCO and Flickr30k datasets.