AI Technology & Industry Review
DeepMind researchers propose Hierarchical Perceiver (HiP), a model that retains the original Perceiver’s ability to process arbitrary modalities but is faster, can scale up to even more inputs/outputs, reduces the need for input engineering, and improves both efficiency and accuracy on classical computer vision benchmarks.
DeepMind trains agents to use keyboard and mouse commands with pixel and Document Object Model (DOM) observations to control computers, achieving state-of-the-art and human-level mean performance across all tasks on the MiniWob++ benchmark.
A research team from Microsoft and NVIDIA leverages the NVIDIA Megatron-LM and Microsoft’s DeepSpeed to create an efficient and scalable 3D parallel system that combines data, pipeline, and tensor-slicing based parallelism, achieving superior zero-, one-, and few-shot learning accuracies and new state-of-the-art results on NLP benchmarks.
A Meta AI research team presents OMNIVORE, a single vision model for various visual modalities that can perform cross-modal generalization and achieves performance at par or better than traditional modality-specific models of the same size.
In the new paper Towards Interactive Language Modeling, a research team from the University of Amsterdam and Meta AI Labs presents a road map detailing the steps to be taken towards interactive language modelling.
In the new paper Efficient Visual Tracking with Exemplar Transformers, ETH Zurich researchers propose Exemplar Transformers for real-time visual object tracking that’s up to 8× faster than other transformer-based models.
A research team from Google Research, University of Cambridge and Alan Turing Institute proposes PolyViT, a single transformer model capable of processing multiple modalities and datasets. PolyViT is parameter-efficient and learns representations that generalize across multiple domains.
On October 11, Microsoft introduced the largest and “the most powerful monolithic transformer language model” trained to date, a 530 billion parameter GPT-3-style generative language model.
A research team from The Swiss AI Lab IDSIA significantly improves the systematic generalization of transformer architectures, achieving accuracy up to 85 percent on the PCFG productivity split, and up to 81 percent on COGS.
A team from Tsinghua University and Microsoft Research Asia proposes Fastformer, an efficient Transformer variant based on additive attention that achieves effective context modelling with linear complexity.
On August 5, WeChat AI and Beijing Jiaotong University system developers released the paper WeChat Neural Machine Translation Systems for WMT21, revealing the architecture of their novel neural machine translation (NMT) system and the strategies they adopted to achieve impressive performance in the WMT21 competition.
A Google Research team draws inspiration from two numerical analysis methods — Hierarchical Matrix (H-Matrix) and Multigrid — to address the quadratic complexity problem of attention mechanisms in transformer architectures, proposing a hierarchical attention scheme that has linear complexity in run time and memory.
A research team from Google proposes ByT5 architecture, a competitive token-free pretrained byte-to-byte transformer that can be straightforwardly adapted to process byte sequences without adding excessive computational cost.
A research team from Google shows that replacing transformers’ self-attention sublayers with Fourier Transform achieves 92 percent of BERT accuracy on the GLUE benchmark with training times seven times faster on GPUs and twice as fast on TPUs.
A research team from DeepMind and Onshape combines a general-purpose language modelling technique and an off-the-shelf data serialization protocol to propose a machine learning model that can automatically generate high-quality sketches for Computer-Aided Design.
A research team from Microsoft Research and Peking University peeps into pretrained transformers and investigates how factual knowledge is stored, proposing a method to identify “knowledge neurons,” which can be utilized to explicitly update and erase facts.
A research team from Google and the University of California, Berkeley calculates the energy use and carbon footprint of large-scale models T5, Meena, GShard, Switch Transformer and GPT-3, and identifies methods and publication guidelines that could help reduce their CO2e footprint.
A team from Huawei, ISCAS and UCAS propose the novel Transformer-iN-Transformer (TNT) for modelling both patch-level and pixel-level representations.
A research team from Facebook AI has proposed a Unified Transformer (UniT) encoder-decoder model that jointly trains on multiple tasks across different modalities and achieves strong performance on seven tasks with a unified set of model parameters.
UC Berkeley, Facebook AI Research and New York University researchers’ Multiple Sequence Alignments (MSA) Transformer surpasses current state-of-the-art unsupervised structure learning methods by a wide margin.
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.
Google Research and DeepMind debut Long-Range Arena (LRA) benchmark for Transformer research on tasks with long sequence lengths.
NeurIPS 2020 Institute of Automation CAS and Microsoft Research Asia paper presents an attention-based decoder that integrates CV object representations
Google recently introduced mT5, a multilingual variant of its “Text-to-Text Transfer Transformer” (T5), pretrained on a new Common Crawl-based dataset covering 101 languages.
A team from Google, University of Cambridge, DeepMind, and Alan Turing Institute have proposed a new type of Transformer dubbed Performer, based on a Fast Attention Via positive Orthogonal Random features (FAVOR+) backbone mechanism.
OpenAI researchers introduce GPT-f, an automated prover and proof assistant for the Metamath formalization language.
Researchers from the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley explored the effect of Transformer model size on training and inference efficiency.