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AI Machine Learning & Data Science Research

Accelerating Quadratic Optimization Up to 3x With Reinforcement Learning

A research team from the University of California, Princeton University and ETH Zurich proposes RLQP, an accelerated QP solver based on operator-splitting QP (OSQP) that uses deep reinforcement learning (RL) to speed up the solver’s convergence rate.

AI Machine Learning & Data Science Nature Language Tech Research

Melbourne U, Facebook & Twitter Expose Novel Numerical Errors in NMT Systems

A research team from the University of Melbourne, Facebook AI, and Twitter Cortex proposes a black-box test method for assessing and debugging the numerical translation of neural machine translation systems in a systematic manner. The approach reveals novel types of errors that are general across multiple state-of-the-art translation systems.

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Quanergy’s 3D LiDAR Selected for First V2X Smart City Deployment in South Korea

On July 20, Quanergy Systems announced its 3D LiDAR solution has been selected to support the development of an Information, Communication, and Technology (ICT) system in Busan, South Korea. The ICT system is a key component of the South Korean government’s strategy to build data driven IoT smart cities. Busan is one of the pilot cities for the initiative.

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 Machine Learning & Data Science Research

Two Lines of Code to Use a 2080Ti to Achieve What Was Previously Only Possible on a V100

As the dynamic computational graph is widely supported by many machine learning frameworks, GPU memory utilization for training on a dynamic computational graph becomes a key specification of these frameworks. In the recently released v1.4, MegEngine provides a way to reduce the GPU memory usage by additional computation using Dynamic Tensor Rematerialization (DTR) technique and further engineering optimization, which makes large batch size training on a single GPU possible.

AI Asia Global News

Tencent’s 7 Billion Dollar AI Supercomputing Center In the Yangtze River Delta Commence Operation

At the World Artificial Intelligence Conference (WAIC) held in Shanghai on July 9, Daosheng Tang, the senior vice exectuvie of Tencent and president of the Tencent cloud and smart industry group, said that the company’s Yangtze River AI Supercomputing Center with RMB 45 billion (approx. USD 7 billion) investment will soon commence operation.

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.