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科学界论文高引第一人易主,Hinton、何恺明进总榜前五!
机器人圈· 2025-08-27 09:41
全世界、所有科学领域都算上,现在最热门的方向就是 AI 了。 图灵奖得主Yoshua Bengio,近日成为了有史以来被引用次数最多的科学家:他的总被引用量高达 973,655 次, 近五年引用量达到 698,008 次。 这项统计来自 AD Scientific Index,这是一个全球性的学术排名和分析平台,旨在评估和展示科学家、研究人员 以及学术机构的科研表现和影响力。 参与这次排名的共计 2,626,749 名科学家,分布在 221 个国家和地区,隶属 24,576 家机构。 排名依据总引用 量和近五年的引用指数进行排序。值得一提的是,这次排名不止 AI 领域,还包括医学等 13 个主要学科和 221 个学术细分学科。 我们再回到 Bengio 的研究。从学术主页来看,Bengio 2014 年提出的 「生成对抗网络(Generative Adversarial Nets)」 引用量已突破 10 万次,甚至超过了他与 Yann LeCun 和 Geoffrey Hinton 合著的经典论 文 「Deep Learning」,不过,后者的引用量同样也超过 10 万次。 | Yoshua Bengio | ...
超97万:Yoshua Bengio成历史被引用最高学者,何恺明进总榜前五
机器之心· 2025-08-25 06:08
| 机器之心报道 | | --- | 机器之心编辑部 全世界、所有科学领域都算上,现在最热门的方向就是 AI 了。 图灵奖得主 Yoshua Bengio,近日成为了有史以来被引用次数最多的科学家:他的总被引用量高达 973,655 次,近五年引用量达到 698,008 次。 这项统计来自 AD Scientific Index,这是一个全球性的学术排名和分析平台,旨在评估和展示科学家、研究人员以及学术机构的科研表现和影响力。 参与这次排名的共计 2,626,749 名科学家,分布在 221 个国家和地区,隶属 24,576 家机构。排名依据总引用量和近五年的引用指数进行排序。值得一 提的是,这次排名不止 AI 领域,还包括医学等 13 个主要学科和 221 个学术细分学科。 我们再回到 Bengio 的研究。从学术主页来看,Bengio 2014 年提出的 「生成对抗网络(Generative Adversarial Nets)」 引用量已突破 10 万次, 甚至超过了他与 Yann LeCun 和 Geoffrey Hinton 合著的经典论文 「Deep Learning」,不过,后者的引用量同样也超过 ...
性能暴涨4%!CBDES MoE:MoE焕发BEV第二春,性能直接SOTA(清华&帝国理工)
自动驾驶之心· 2025-08-18 23:32
Core Viewpoint - The article discusses the CBDES MoE framework, a novel modular expert mixture architecture designed for BEV perception in autonomous driving, addressing challenges in adaptability, modeling capacity, and generalization in existing methods [2][5][48]. Group 1: Introduction and Background - The rapid development of autonomous driving technology has made 3D perception essential for building safe and reliable driving systems [5]. - Existing solutions often use fixed single backbone feature extractors, limiting adaptability to diverse driving environments [5][6]. - The MoE paradigm offers a new solution by enabling dynamic expert selection based on learned routing mechanisms, balancing computational efficiency and representational richness [6][9]. Group 2: CBDES MoE Framework - CBDES MoE integrates multiple structurally heterogeneous expert networks and employs a lightweight self-attention router (SAR) for dynamic expert path selection [3][12]. - The framework includes a multi-stage heterogeneous backbone design pool, enhancing scene adaptability and feature representation [14][17]. - The architecture allows for efficient, adaptive, and scalable 3D perception, outperforming strong single backbone baseline models in complex driving scenarios [12][14]. Group 3: Experimental Results - In experiments on the nuScenes dataset, CBDES MoE achieved a mean Average Precision (mAP) of 65.6 and a NuScenes Detection Score (NDS) of 69.8, surpassing all single expert baselines [37][39]. - The model demonstrated faster convergence and lower loss throughout training, indicating higher optimization stability and learning efficiency [39][40]. - The introduction of load balancing regularization significantly improved performance, with the mAP increasing from 63.4 to 65.6 when applied [42][46]. Group 4: Future Work and Limitations - Future research may explore patch-wise or region-aware routing for finer granularity in adaptability, as well as extending the method to multi-task scenarios [48]. - The current routing mechanism operates at the image level, which may limit its effectiveness in more complex environments [48].
刚刚,何恺明官宣新动向~
自动驾驶之心· 2025-06-26 10:41
Core Viewpoint - The article highlights the significant impact of Kaiming He joining Google DeepMind as a distinguished scientist, emphasizing his dual role in academia and industry, which is expected to accelerate the development of Artificial General Intelligence (AGI) at DeepMind [1][5][8]. Group 1: Kaiming He's Background and Achievements - Kaiming He is renowned for his contributions to computer vision and deep learning, particularly for introducing ResNet, which has fundamentally transformed deep learning [4][18]. - He has held prestigious positions, including being a research scientist at Microsoft Research Asia and Meta's FAIR, focusing on deep learning and computer vision [12][32]. - His academic credentials include a tenure as a lifelong associate professor at MIT, where he has published influential papers with over 713,370 citations [18][19]. Group 2: Impact on Google DeepMind - Kaiming He's expertise in computer vision and deep learning is expected to enhance DeepMind's capabilities, particularly in achieving AGI within the next 5-10 years, as stated by Demis Hassabis [7][8]. - His arrival is seen as a significant boost for DeepMind, potentially accelerating the development of advanced AI models [5][39]. Group 3: Research Contributions - Kaiming He has published several highly cited papers, including works on Faster R-CNN and Mask R-CNN, which are among the most referenced in their fields [21][24]. - His recent research includes innovative concepts such as fractal generative models and efficient one-step generative modeling frameworks, showcasing his continuous contribution to advancing AI technology [36][38].
刚刚,何恺明官宣入职谷歌DeepMind!
猿大侠· 2025-06-26 03:20
Core Viewpoint - Kaiming He, a prominent figure in AI and computer vision, has officially joined Google DeepMind as a distinguished scientist while retaining his position as a tenured associate professor at MIT, marking a significant boost for DeepMind's ambitions in artificial general intelligence (AGI) [2][5][6]. Group 1: Kaiming He's Background and Achievements - Kaiming He is renowned for his contributions to deep learning, particularly for developing ResNet, which has fundamentally transformed the trajectory of deep learning and serves as a cornerstone for modern AI models [5][17]. - His academic influence is substantial, with over 713,370 citations for his papers, showcasing his impact in the fields of computer vision and deep learning [17][18]. - He has received numerous prestigious awards, including the best paper awards at major conferences such as CVPR and ICCV, highlighting his significant contributions to the field [23][26]. Group 2: Implications of His Joining DeepMind - Kaiming He's expertise in computer vision and deep learning is expected to accelerate DeepMind's efforts towards achieving AGI, a goal that Demis Hassabis has indicated could be realized within the next 5-10 years [8][9]. - His recent research focuses on developing models that learn representations from complex environments, aiming to enhance human intelligence through more capable AI systems [16][17]. - The addition of Kaiming He to DeepMind is seen as a strategic advantage, potentially leading to innovative breakthroughs in AI model development [6][37].
何恺明新身份:谷歌DeepMind杰出科学家
机器之心· 2025-06-26 00:30
Core Viewpoint - The article discusses the recent news of Kaiming He joining Google as a part-time Distinguished Scientist at DeepMind, highlighting his significant contributions to the field of AI and computer vision [2][4][24]. Group 1: Kaiming He's Background and Achievements - Kaiming He achieved the highest score in the 2003 Guangdong Province college entrance examination and was admitted to Tsinghua University [8]. - He completed his PhD at the Chinese University of Hong Kong under the supervision of Xiaoguo Tang and has held positions at Microsoft Research Asia, Facebook AI Research, and MIT [9]. - His research has received multiple awards, including the best paper award at CVPR in 2009 and 2016, and he has over 710,000 citations according to Google Scholar [10][12]. Group 2: Research Contributions - Kaiming He's most notable work includes the ResNet paper published in 2016, which has been cited over 280,000 times and is considered one of the most cited papers of the 21st century [15][18]. - His research addresses the gradient propagation problem in deep networks, establishing fundamental components for modern deep learning models [18][19]. - He has also contributed to the development of the Masked Autoencoders model, which has gained popularity in the computer vision community [20]. Group 3: Future Prospects at Google - The article expresses anticipation for Kaiming He's potential contributions at Google, particularly in the area of generative modeling, as suggested by his recent research [6][24].
专访张祥雨:多模态推理和自主学习是未来的 2 个 「GPT-4」 时刻
海外独角兽· 2025-06-08 04:51
本期内容是拾象 CEO 李广密对大模型公司阶跃星辰首席科学家张祥雨的访谈。 张祥雨专注于多模态领域,他提出了 DreamLLM 多模态大模型框架,这是业内最早的图文生成理解 一体化的多模态大模型架构之一,基于这个框架,阶跃星辰发布了中国首个千亿参数原生多模态大 模型 Step-1V。此外,他的学术影响力相当突出,论文总引用量已经超过了 37 万次。 一直以来,业界都相当期待一个理解、生成一体化的多模态,但直到今天这个模型还没出现,如何 才能达到多模态领域的 GPT-4 时刻?这一期对谈中,祥雨结合自己在多模态领域的研究和实践历 程,从纯粹的技术视角下分享了自己对多模态领域关键问题的全新思考,在他看来,虽然语言模型 领域的进步极快,但多模态生成和理解的难度被低估了: • 接下来 2-3 年,多模态领域会有两个 GPT-4 时刻:多模态推理和自主学习; • o1 范式的技术本质在于激发出 Meta CoT 思维链:允许模型在关键节点反悔、重试、选择不同分 支,使推理过程从单线变为图状结构。 目录 01 研究主线: 重新回归大模型 • 多模态生成理解一体化难以实现的原因在于,语言对视觉的控制能力弱,图文对齐不精确, ...
亚裔 AI 人才的硅谷晋升之路,被一张绿卡阻断了?
3 6 Ke· 2025-04-28 11:23
Core Viewpoint - The article highlights the precarious situation faced by skilled immigrants in the U.S. tech industry, particularly in light of tightening immigration policies, as exemplified by the case of Kai Chen, a prominent AI researcher who was forced to leave the U.S. after her green card application was denied [2][4][5]. Group 1: Impact of Immigration Policies - The tightening of immigration policies under the Trump administration has created a new barrier for skilled workers in the tech industry, particularly affecting those on H1B visas [2][6][18]. - Kai Chen's experience reflects a broader trend where even highly qualified individuals with significant contributions to their companies can suddenly find themselves at risk of deportation [4][5][6]. - The article notes that over 1,000 international students have had their visas revoked, illustrating the widespread impact of these policies across various sectors [16][18]. Group 2: Demographics and Contributions of Asian Talent - Asian representation in major U.S. AI companies is significant, with Asians making up 45.7% of Google's workforce, surpassing the percentage of white employees [7][9]. - The article emphasizes that while Asian talent, particularly of Indian and Chinese descent, has been rising in the tech industry, they often face challenges in career advancement due to office politics [9][10]. - Despite these challenges, the AI sector has provided new opportunities for Asian professionals, allowing them to leverage their technical skills for career growth [10][11]. Group 3: Future Prospects for Talent - The article discusses the potential for skilled workers like Kai Chen to seek opportunities outside the U.S., as companies in Europe and China are actively recruiting top talent [19][20]. - Major Chinese tech firms are launching initiatives to attract high-end talent, indicating a shift in where skilled professionals may choose to work in the future [20][21]. - The narrative suggests that while the U.S. has historically been a magnet for talent, the current political climate may lead to a redistribution of skilled workers globally [19][22].
「AI三杰」背后的广东隐忧
投资界· 2025-02-17 01:33
孙不熟专栏,在这里读懂城市、学会买房。 以下文章来源于城市战争 ,作者孙不熟 城市战争 . 为何都不在广东创业? 来源 | 城市战争 (ID:sunbushu123) 最近在网上看多一个说法:中国"AI三杰"都是广东人,但都没有在广东创业和发展。 公开资料显示,这三位蜚声全球的AI大牛都很年轻,其中两个是8 0后、一个是9 0后,一 个在杭州上大学和创业,一个在北京上大学和创业,另一个在美国MIT任教。 作者 | 孙不熟 梁文锋:广东湛江人 以一己之力干崩美股 De e ps e e k是春节期间火遍全球的一款国产大模型,成功地将大模型的训练成本断崖式降 低,被称之为"AI界的拼多多",全球为之震惊,甚至有媒体称他"以一己之力干崩了美 股"。 梁文锋是De e p s e e k的创始人,1985年出生于广东湛江吴川的偏远农村,1 7岁就以优异 成绩考上浙江大学信息与电子工程系,并在浙大拿下硕士学位。 毕业后的梁文锋没有选择像很多同学那样进大厂当码农,而是选择自主创业,从事当时 并不被看好的全自动量化交易,就是使用数学模型和计算机方法来指导股票交易。 网友口中的"AI三杰"指的是De e ps e e k的创 ...