AI Technology & Industry Review
Technical review of the newest machine intelligence research.
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.
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
A research team from DeepMind and University College London have released Alchemy, a novel open-source benchmark for meta-RL research.
Researchers from the University of Wisconsin-Madison, UC Berkeley, Google Brain and American Family Insurance propose Nyströmformer, an adaption of the Nystrom method that approximates standard self-attention with O(n) complexity.
The research develop machine learning methods that enable virtual agents (such as avatars from a computer game) to communicate non-verbally.
In a bid to solve the temporal generalization problem of modern language models, a team of DeepMind researchers propose it’s time to develop adaptive language models that will remain up-to-date in our ever-changing world.
A new study by University Tübingen introduces the world’s largest unified eye dataset with over 20 million human eye images captured using head-mounted eye trackers.
To track progress in natural language generation (NLG) models, 55 researchers from more than 40 prestigious institutions have proposed GEM (Generation, Evaluation, and Metrics), a “living benchmark” NLG evaluation environment.
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) kicked off today as a virtual conference. The organizing committee announced the Best Paper Awards and Runners Up during this morning’s opening ceremony.
A bold Carnegie Mellon University (CMU) team recently explored the prospect of using AI to review AI papers.
Researchers from the University of Sheffield, Beihang University, and Open University’s Knowledge Media Institute have proposed a transfer learning approach that can automatically process historical texts at a semantic level to generate modern language summaries.
A new study by the Georgia Institute of Technology and Facebook AI introduces TT-Rec, a way to drastically compress the size of memory-intensive Deep Learning Recommendation Models (DLRM) and make them easier to deploy at scale.
A recent study by the Google Brain Team proposes a new way of programming automated machine learning (AutoML) based on symbolic programming.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
A new study by NVIDIA, University of Toronto, McGill University and the Vector Institute introduces an efficient neural representation that enables real-time rendering of high-fidelity neural SDFs for the first time while delivering SOTA geometry reconstruction quality.
Researchers from University of California, Merced and Microsoft have introduced ZeRO-Offload, a novel heterogeneous DL training technology that enables training of multi-billion parameter models on a single GPU without any model refactoring.
Researchers from Naver AI Lab says they’ve found a computationally efficient re-labelling strategy that fixes a significant flaw in popular image classification benchmark ImageNet.
Is it possible for machines to understand and integrate human emotions in visual arts? Meet ArtEmis, a new large-scale dataset of emotional reactions and explanations for visual artworks.
Analyzing papers and patent data collected from top AI journals and conferences, 44 in total from 2011 to 2020, Tsinghua University’s new AI development report revealed new statistical insights.
This paper is the first to systematically study the security of Multi-Sensor Fusion (MSF) based localization in high-autonomy Autonomous Vehicles (AVs). Researchers design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities.
Researchers from Drexel University, Northeastern University and IT University Copenhagen explore how humans interact with AI in such contexts, with a focus on computer games.
In a new paper, Stanford University researchers dispel the assumption that boosting deep learning model performance on ImageNet translates to better performance on medical imaging tasks.
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.
On January 18, in efforts to understand and manage extreme weather events, McGill University announced a new interdisciplinary project to combine deep learning (DL) with social network analysis (SNA).
Researchers from the University of Texas, Austin have proposed a novel framework called Training-Free Neural Architecture Search (TE-NAS) for “training-free” neural architecture search.
In a new paper, researchers from Max Planck Institute for Informatics and Facebook Reality Labs propose an end-to-end trainable method that enables re-rendering of humans from one single image.
A paper in the Journal of Artificial Intelligence Research goes a step further, warning that it could become fundamentally impossible to control a superintelligent AI.
Google Brain’s Switch Transformer language model packs a whopping 1.6 trillion parameters while effectively controlling computational cost. The model achieved a 4x pretraining speedup over a strongly tuned T5-XXL baseline.
Of the 2997 submissions, 860 papers have made it to ICLR 2021, for an acceptance rate of 28.7 percent — slightly higher than last year’s 26.5 percent.
Recent AI research on speech separation has explored ways to associate lip motions in videos with audio, but this approach suffers when speakers’ lips are occluded, which they often are in busy multi-speaker environments.
VOGUE, an AI-powered optimization method that deforms garments according to a given body shape while preserving pattern and material details to deliver state-of-the-art photorealistic, high-resolution try-on images.
AI’s acceleration of scientific discoveries continues - last month DeepMind’s AlphaFold solved a grand challenge in biology, and now an international AI research team has targeted materials science.
Turing Award winner Geoffrey Everest Hinton spoke about his recent research, his quest to understand learning in the brain and why he sees unsupervised learning as the future of AI.
Researchers propose a novel framework and hierarchical predictive model that learns to identify what is predictable from unlabelled video.
A new model surpassed human baseline performance on the challenging natural language understanding benchmark.
Synced has invited Mr. Brian Tse to share his insights about the current development and future trends of artificial intelligence.
OpenAI has trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
The novel approach tackles dynamic 3D human-body synthesis from a sparse set of camera views, bettering existing methods on key metrics by significant margins.
Researchers proposed PGDrive, a driving simulator designed to evaluate and improve end-to-end driving agents’ generalization abilities.
Synced has invited Mr. Tatsuya Nagata, the COO of Navier Inc., to share his insights about the current development and future trends of artificial intelligence.