Tag: ML

AI Computer Vision & Graphics Machine Learning & Data Science Research

Graph Kernel Attention Transformers: Toward Expressive and Scalable Graph Processing

A research team from Google Brain, Columbia University and University of Oxford proposes Graph Kernel Attention Transformers (GKATs), a new class of graph neural network that achieves greater expressive power than SOTA GNNs while reducing computation burdens.

AI Machine Learning & Data Science Research

Baidu’s Knowledge-Enhanced ERNIE 3.0 Pretraining Framework Delivers SOTA NLP Results, Surpasses Human Performance on the SuperGLUE Benchmark

A research team from Baidu proposes ERNIE 3.0, a unified framework for pretraining large-scale, knowledge-enhanced models that can easily be tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning, and achieves state-of-the-art results on NLP tasks.

AI Machine Learning & Data Science Research

New Study Proposes Quantum Belief Function, Achieves Exponential Time Acceleration

A research team from the University of Electronic Science and Technology of China, Chinese Academy of Sciences, School of Education Shaanxi Normal University, Japan Advanced Institute of Science and Technology and ETH Zurich encodes the basic belief assignment (BBA) into quantum states and implements them on a quantum circuit, aiming to utilize quantum computation characteristics to better handle belief functions.

AI Computer Vision & Graphics Machine Learning & Data Science Popular Research

Facebook & UC Berkeley Substitute a Convolutional Stem to Dramatically Boost Vision Transformers’ Optimization Stability

A research team from Facebook AI and UC Berkeley finds a solution for vision transformers’ optimization instability problem by simply using a standard, lightweight convolutional stem for ViT models. The approach dramatically increases optimizer stability and improves peak performance without sacrificing computation efficiency.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Video Swin Transformer Improves Speed-Accuracy Trade-offs, Achieves SOTA Results on Video Recognition Benchmarks

A research team from Microsoft Research Asia, University of Science and Technology of China, Huazhong University of Science and Technology, and Tsinghua University takes advantage of the inherent spatiotemporal locality of videos to present a pure-transformer backbone architecture for video recognition that leads to a better speed-accuracy trade-off.

AI Machine Learning & Data Science Research

New Milestone for Deep Potential Application: Predicting the Phase Diagram of Water

A research team from Princeton University, the Institute of Applied Physics and Computational Mathematics and the Beijing Institute of Big Data Research uses the Deep Potential (DP) method to predict the phase diagram of water from ab initio quantum theory, from low temperature and pressure to about 2400 K and 50 GPa. The paper was published in leading physics journal Physical Review Letters and represents an important milestone in the application of DP.

AI Machine Learning & Data Science Nature Language Tech Research

Google Researchers Merge Pretrained Teacher LMs Into a Single Multilingual Student LM Via Knowledge Distillation

A Google Research team proposes MergeDistill, a framework for merging pretrained teacher LMs from multiple monolingual/multilingual LMs into a single multilingual task-agnostic student LM to leverage the capabilities of the powerful language-specific LMs while still being multilingual and enabling positive language transfer.

AI Machine Learning & Data Science Research

Pieter Abbeel Team’s Decision Transformer Abstracts RL as Sequence Modelling

A research team from UC Berkeley, Facebook AI Research and Google Brain abstracts Reinforcement Learning (RL) as a sequence modelling problem. Their proposed Decision Transformer simply outputs optimal actions by leveraging a causally masked transformer, yet matches or exceeds state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks.

AI Machine Learning & Data Science Research

What Matters in Adversarial Imitation Learning? Google Brain Study Reveals Valuable Insights

A research team from Google Brain conducts a comprehensive empirical study on more than fifty choices in a generic adversarial imitation learning framework and explores their impacts on large-scale (>500k trained agents) continuous-control tasks to provide practical insights and recommendations for designing novel and effective AIL algorithms.

AI Machine Learning & Data Science Research

Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed Structures

A research team from OneFlow and Microsoft takes a step toward automatic deep neural network structure design, exploring unsupervised structure-learning and leveraging the efficient coding principle, information theory and computational neuroscience to design structure learning without label information.

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google & Rutgers’ Aggregating Nested Transformers Yield Better Accuracy, Data Efficiency and Convergence

A research team from Google Cloud AI, Google Research and Rutgers University simplifies vision transformers’ complex design, proposing nested transformers (NesT) that simply stack basic transformer layers to process non-overlapping image blocks individually. The approach achieves superior ImageNet classification accuracy and improves model training efficiency.

AI Machine Learning & Data Science Research

NYU, Facebook & CIFAR Present ‘True Few-Shot Learning’ for Language Models Whose Few-Shot Ability They Say Is Overestimated

A research team from New York University, Facebook AI, and a CIFAR Fellow in Learning in Machines & Brains raise doubts regarding large-scale pretrained language models’ few-shot learning abilities. The researchers re-evaluate such abilities with held-out examples unavailable, which they propose constitutes “true few-shot learning.”