AI Technology & Industry Review
In the new paper Non-deep Networks, a research team from Princeton University and Intel Labs argues it is possible to achieve high performance with “non-deep” neural networks, presenting ParNet (Parallel Networks), a novel 12-layer architecture that achieves performance competitive with its state-of-the-art deep counterparts.
A DeepMind research team explores the problem of how to train agents to collaborate well with novel human partners without using human data and presents Fictitious Co-Play (FCP), a surprisingly simple approach designed to address this challenge.
Facebook AI and NYU researchers challenge the conventional wisdom regarding interpolation in machine learning, arguing that interpolation almost never happens on high-dimensional datasets.
In a paper currently under double-blind review for ICLR 2022, researchers propose StyleNeRF, a 3D-aware generative model that can synthesize high-resolution images at interactive rates while preserving high-quality 3D consistency, and can even generalize to unseen views with control on styles and poses.
A research team from the University of Southern California and Google proposes TOME, a “mention memory” approach to factual knowledge extraction for NLU tasks. A transformer model with attention over a semi-parametric representation of the entire Wikipedia text corpus, TOME can extract information without supervision and achieves strong performance on multiple open-domain question answering benchmarks.
A Google Research team introduces Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models and discrete diffusion models that can generate variables in an arbitrary order and upscale variables.
A Google Research team conducts a systematic exploration comprising more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with parameters ranging from 10 million to 10 billion, evaluated on more than 20 downstream image recognition tasks, aiming to capture the nonlinear relationships between performance on upstream and downstream tasks.
A NVIDIA and Aalto University research team presents StyleGAN3, a novel generative adversarial network (GAN) architecture where the exact sub-pixel position of each feature is exclusively inherited from the underlying coarse features, enabling a more natural transformation hierarchy and advancing GAN-based animation generation.
A research team proposes ConvMixer, an extremely simple model designed to support the argument that the impressive performance of vision transformers (ViTs) is mainly attributable to their use of patches as the input representation. The study shows that ConvMixer can outperform ViTs, MLP-Mixers and classical vision models.
According to Reuters, the Pentagon’s former software chief Nicolas Chaillan told the Financial Times on Sunday that “We have no competing fighting chance against China in 15 to 20 years. Right now, it’s already a done deal; it is already over, in my opinion.”
An Apple research team performs a comparative analysis on a contrastive self-supervised learning (SSL) algorithm (SimCLR) and a supervised learning (SL) approach for simple image data in a common architecture, shedding light on the similarities and dissimilarities in their learned visual representation patterns.
A research team from Facebook AI and Carnegie Mellon University presents VideoCLIP, a contrastive approach for pretraining a unified model for zero-shot video and text understanding without requiring annotated data for downstream tasks.
A Google Research team presents compressive variants of SimCLR and BYOL that yield better and more robust visual representations.
A DeepMind research team proposes Faster Improvement Rate PBT (FIRE PBT) for Population Based Training (PBT), an automated hyperparameter tuning method for neural network training. The novel approach achieves faster improvement rates and better long-term performance.
An Oxford University research team presents PASS, a large (1.28M) image collection excluding humans, created as an ImageNet replacement for self-supervised pretraining without technical, ethical or legal issues.
A research team from ETH Zurich and NVIDIA proposes a training framework that enables fast policy generation for real-world robotic tasks — training time can be reduced by multiple orders of magnitude using massive parallelism on a single workstation GPU.
In the paper Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers, a research team from New York University and the University of North Carolina at Chapel Hill uses centered kernel alignment (CKA) to measure the similarity of representations across layers and explore how fine-tuning changes transformers’ learned representations.
In the paper Introducing Symmetries to Black Box Meta Reinforcement Learning, a research team from DeepMind and The Swiss AI Lab IDSIA explores the role of symmetries in meta generalization and shows that introducing more symmetries to black-box meta-learners can improve their ability to generalize to unseen action and observation spaces, tasks, and environments.
A research team from New York University and the University of British Columbia proposes deviation-based learning, a novel approach for training recommender systems that learns user knowledge by observing whether they follow or deviate from recommendations.
A Google AI research team explores zero-label learning (training with synthetic data only) in natural language processing, and introduces Unsupervised Data Generation (UDG), a training data creation procedure designed to synthesize high-quality training data without human annotations.
A team from University of Massachusetts Amherst and Google Research proposes STraTA, an approach that combines task augmentation and self-training to leverage unlabelled data and improve sample efficiency and performance on NLP tasks.
In the paper Detoxifying Language Models, a DeepMind research team critically discusses toxicity evaluation and mitigation for contemporary transformer-based English large language models and provides insights on safer model use and deployment.
A research team from Hugging Face introduces a block pruning approach targeting both small and fast models, which learns to eliminate full components of the original model while effectively dropping a large number of attention heads.
A research team from DeepMind proposes a bootstrapped meta-learning algorithm that overcomes the meta-optimization problem and myopic meta objectives, and enables the meta-learner to teach itself.
A new MIT CSAIL paper presents an alternative, hierarchical approach to sequence-to-sequence learning with quasi-synchronous grammars to improve sequence-to-sequence models’ compositional generalization on diagnostic tasks.
A research team from UC Berkeley takes a causal perspective on representation learning, formalizing non-spuriousness, efficiency and disentanglement representation learning desiderata using causal notions.
A research team from Carnegie Mellon University, Google Brain and UC Berkeley proposes a robust predictable control (RPC) method for learning reinforcement learning policies that use fewer bits of information. This simple and theoretically-justified algorithm achieves much tighter compression, is more robust, and generalizes better than prior methods, achieving up to 5× higher rewards than a standard information bottleneck.
A research team from Purdue University presents a self-adaptive algorithm for optimal deep neural network design. The adaptive network enhancement (ANE) method learns not only from given information (data, function, PDEs) but also from the current computer simulation.
MIT researchers present an automated, objective and transparent data-driven method for measuring media bias. The study analyses roughly a million articles from about a hundred newspapers for bias on various news topics, maps the newspapers into a two-dimensional media bias landscape, and shows that the data-driven results agree well with human-judgement classifications.
Researchers from Robotics at Google propose reformulating behavioural cloning using implicit models and show that this simple change can lead to remarkable improvements in performance across a wide range of contact-rich robot policy learning.
Researchers from Instituto de Telecomunicações, DeepMind, Institute of Systems and Robotics, Instituto Superior Técnico and Unbabel propose “∞-former” — a transformer model with unbounded long-term memory (LTM) that can attend to arbitrarily long contexts.
In the paper ReGen: Reinforcement Learning for Text and Knowledge Base Generation Using Pretrained Language Models, IBM researchers present ReGen, a bidirectional generation of text and graph that leverages reinforcement learning to push the performance of text-to-graph and graph-to-text generation tasks to a higher level.
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 research team from Stanford University introduces BEHAVIOR, a benchmark for embodied AI with 100 realistic, diverse and complex everyday household activities in simulation. BEHAVIOR addresses challenges such as definition, instantiation in a simulator, and evaluation; and pushes the state-of-the-art by adding new types of state changes.
A research team from the University of Cambridge and Facebook AI proposes ProoFVer, a proof system for fact verification using natural logic. ProoFVer provides faithful explanations while outperforming state-of-the-art fact verification models.
A Nvidia research team presents Isaac Gym — a high-performance robotics simulation platform that runs an end-to-end GPU accelerated training pipeline. Compared to conventional RL training methods that use a CPU-based simulator and GPU for neural networks, Isaac Gym achieves training speedups of 2-3 orders of magnitude on continuous control tasks.
A DeepMind research team proposes Collect and Infer, a novel paradigm that explicitly models Reinforcement Learning (RL) as data collection and knowledge inference to dramatically boost RL data efficiency.
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.
A Google Brain research team explores the internal representation structures of ViTs and CNNs on image classification tasks, providing insights on key differences between the two approaches.
A LinkedIn research team evaluates deep natural language processing (NLP) on various representative search engine tasks to provide insights for the development of industry search engines.