AI Technology & Industry Review
This paper carefully engineers artificial-language learning experiments to replicate sources of information infants learn from and under what conditions.
NVIDIA researchers propose a novel vid2vid framework that utilizes all past generated frames during rendering.
NeurIPS desk-rejects (papers passed over without review) has become “a super noisy process,” complains a post in the Reddit Machine Learning group.
Researchers launched the ProtTrans Project, which provides an outstanding model for protein pretraining.
Researchers revealed a circuit mechanism, in which BNST modulated the NAc, exerting a direct effect on anxiety.
TaBERT-powered neural semantic parsers showed performance improvements on the challenging benchmark WikiTableQuestions and demonstrated competitive performance on the text-to-SQL dataset Spider.
Researchers proposed a Taylor Expansion Policy Optimization (TayPO) framework that combines two leading algorithmic improvement methods.
DeepMind introduced a new approach designed to improve the generalizability and efficiency of algorithms represented by neural networks.
DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.
A team of researchers proposed a novel three-stage learning framework that includes scene context to generate long-term 3D human motion prediction when given a single scene image and 2D pose histories.
Interactive sensors and actuators, with the “intelligence” of sensing the environment and responding accordingly, are essential for human-machine interaction. However, it remains a critical challenge for a long time.
NLE is a procedurally generated environment for testing the robustness and systematic generalization of RL agents.
A team of researchers from Princeton, DeepMind and New York University have introduced a new method that extracts symbolic representations from deep learning models by introducing strong induction biases.
This paper presents a novel contrastive framework for unsupervised graph representation learning.
The researchers introduce sinusoidal representation networks (SIRENs) as a method for leveraging periodic activation functions for implicit neural representations.
DeepMind researchers released several new models and a tutorial for their dm_control software stack for physics-based simulation and reinforcement learning environments using MuJoCo physics.
Researchers have proposed the use of Fourier feature mapping with MLPs in order to learn high-frequency functions in low-dimensional problem domains.
Following on the February release of its contrastive learning framework SimCLR, the same team of Google Brain researchers guided by Turing Award honouree Dr. Geoffrey Hinton has presented SimCLRv2, an upgraded approach that boosts the SOTA results by 21.6 percent.
Researchers from the University of Washington, Salesforce Research and Allen Institute for Artificial Intelligence have introduced a graph-based method that retrieves reasoning paths to boost multi-hop open-domain question answering.
Cruciform Thin-film Microrobot (CTM) could realize double-modal motion and environmental adaptive functions through a programmable external magnetic field.
A team of researchers from Google Brain have proposed a rethink of the dominant computer vision paradigm of pre-training.
Researchers have proposed a novel framework that learns fast and dynamic character interactions.
Researchers from Fudan University and Microsoft have proposed a novel seq2seq architecture that generates dance sequences for music clips running a minute or longer.
A team of researchers from Facebook and UC Berkeley has proposed a new paradigm for computer vision.
Researchers from the University of Tokyo and Google Research have proposed a new metric for RL performance and novel BREMEN algorithm designed to manage the costs and risks of new policy deployment.
In the seminal 1996 paper Light Field Rendering, Levoy and Hanrahan describe a representation for light fields that allows for both efficient creation and display.
A team of researchers from CMU and Technion recently introduced a new system, Penrose, that can turn complex mathematical notations into various styles of simple diagrams.
A team of researchers from the Chinese Academy of Sciences and the City University of Hong Kong has introduced a local-to-global approach that can generate lifelike human portraits from relatively rudimentary sketches.
Researchers add syntactic biases to determine whether and where they can help BERT achieve better understanding.
Facebook this week released Detection Transformers (DETR), a new approach for object detection and panoptic segmentation tasks that uses a completely different architecture than previous object detection systems.
The research team proposes that colourization performance can be improved dramatically at the instance level for a few reasons.
The team introduces photon sources fabricated in silicon that meet a variety of requirements for scalable quantum photonics: high purity, high heralding efficiency, and high indistinguishability.
We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain, given an exemplar image.
The proposed method outperforms supervised methods and unsupervised translation methods on restoring real photos.
Enter Plan2Explore — a self-supervised RL agent designed to quickly generalize to unseen tasks in a zero or few-shot manner.
A “data echoing” technique that enables these time-consuming upstream training stages to also benefit from accelerators.
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
Researchers from the University of Bristol, the University of Toronto and the University of Catania explain how they created Epic-Kitchens and introduce new baselines that emphasize the multimodal nature of the largest such egocentric video benchmark.
A team of researchers from Russian AI startup OSAI recently introduced the real-time neural network TTNet, designed for processing high-resolution table tennis videos with both temporal and spatial data.
Researchers “posit that the universes of knowledge and experience available to NLP models can be defined by successively larger world scopes: from a single corpus to a fully embodied and social context.”