87% ImageNet Accuracy, 3.8ms Latency: Google’s MobileNetV4 …
WEBApr 18, 2024 · Efficient on-device neural networks offer rapid, real-time, and interactive experiences while safeguarding private data from public internet exposure. Yet, the computational limitations of mobile devices present a formidable challenge in maintaining a delicate balance between accuracy and efficiency. Addressing this challenge head-on, a recent paper titled "MobileNetV4 - Universal Models for ...
IBM Closes China Research Laboratory | Synced
WEBJan 25, 2021 · IBM has closed off its Beijing-based China Research Laboratory (CRL) after 25 years of operation. Insiders familiar with the matter posted confirmations on Chinese social media sites on January 23. In an official response released today, IBM explained that the move aimed to “transform the R&D layout in China, as to support Chinese clients ...
VidPress: Baidu’s New AI Tool Creating Videos from a URL with …
WEBApr 30, 2020 · Given a URL as input, VidPress can automate the creation process from choosing clips that fit the topic to knitting video content with AI-synthesized narratives. Deployed on Baidu’s short video app Haokan, VidPress has provided a major boost in terms of quantity and quality. VidPress can produce over 1,000 news videos per …
OpenAI Unveils 175 Billion Parameter GPT-3 Language Model
WEBMay 29, 2020 · This is an updated version. When it comes to large language models, it turns out that even 1.5 billion parameters is not large enough. While that was the size of the GPT-2 transformer-based language model that OpenAI released to much fanfare last year, today the San Francisco-based AI company outdid itself, announcing the upgraded GPT-3 with a whopping 175 billion parameters.
China’s GPT-3? BAAI Introduces Superscale Intelligence Model …
WEBMar 23, 2021 · The BAAI research team summarizes some of Wu Dao 1.0’s key contributions: Wu Dao – Wen Yuan introduces the open-source Chinese pretraining model (CPM). Based on CPM, the CPM-Distill model reduces language confusion by 38 percent and achieves better results on downstream tasks. Wu Dao – Wen Lan is the first Chinese generic multimodal ...
DeepMind, Google Brain & World Chess Champion Explore How …
WEBNov 24, 2021 · In the new paper Acquisition of Chess Knowledge in AlphaZero, DeepMind and Google Brain researchers and former World Chess Champion Vladimir Kramnik explore how and to what extent human knowledge is acquired by AlphaZero and how chess concepts are represented in its network. They do this via comprehensive concept probing, behavioural analysis ...
Pieter Abbeel Team’s Decision Transformer Abstracts RL as …
WEBJun 9, 2021 · Recent studies have shown that transformers can model high-dimensional distributions of semantic concepts at scale, opening up the intriguing possibility of formalizing sequential decision-making problems as reinforcement learning (RL). New research from a UC Berkeley, Facebook AI Research and Google Brain team that includes esteemed Belgian professor Pieter Abbeel explores whether generative ...
Alibaba’s New AI Chip Can Process Nearly 80K Images Per Second
WEBSep 25, 2019 · Given an inference image classification benchmark test on ResNet-50, Hanguang 800’s peak performance is 78,563 images per second (IPS). Zhang says the Hanguang 800 is 15 times more powerful than the NVIDIA T4 GPU, and 46 times more powerful than the NVIDIA P4 GPU. The chip’s peak efficiency is 500 IPS/W. The Pingtouge chip development team ...
DeepMind Decodes the Puzzle of ‘ Grokking ’ In Neural Network ...
WEBSep 15, 2023 · One of the intriguing puzzles within the realm of neural network generalization is a phenomenon known as "grokking." It involves a neural network achieving perfect training accuracy but displaying poor generalization capabilities. Interestingly, it has been observed that further training can transform a network experiencing this phenomenon into one that exhibits perfect generalization—a ...
Pieter Abbeel Team Proposes Task-Agnostic RL Method to Auto …
WEBApr 21, 2021 · The real world is complex and ever-changing. Applying deep learning (DL) techniques to complex control tasks relies on learning in simulations before transferring models to the real world. But there is a problematic "reality gap" associated with such transfers, as it is difficult for simulators to accurately capture or predict the dynamics and visual properties
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