Economics of Apps - University of Oxford, 15 Oct 2010Volker Hirsch
The slides to my talk given at ForumOxford's Future of Technologies conference on 15 October 2010 at the University of Oxford. And, yes, the imagery is there in full glory... :)
"Comparing Variable Importance from Ensemble and Deep Learning Methods for AdTech Data"
Variable Importance brings interpretability to popular black box modeling techniques. In this talk we study performance of popular ensemble techniques like Random Forest, Gradient Boosting with GLM. We observe certain traits that get magnified by non-linear techniques like Deep Learning that are otherwise missed by GBM or Random Forest.
We describe Open Source Scalable Machine Learning package, H2O which through ease-of-use and speed makes comparisons and picking best-of-breed and ensembles more natural. H2O's implementation of these algorithms tracks popular open source and text book implementations closely.
Read: issuu.com/shuweigoh/docs/skymind
At Skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. We offer state-of-the-art, flexible, scalable deep learning for industry. Deep learning is becoming an important tool set for natural-language processing (NLP), computer vision, database predictions, pattern recognition, image/video processing and fraud detection.
Economics of Apps - University of Oxford, 15 Oct 2010Volker Hirsch
The slides to my talk given at ForumOxford's Future of Technologies conference on 15 October 2010 at the University of Oxford. And, yes, the imagery is there in full glory... :)
"Comparing Variable Importance from Ensemble and Deep Learning Methods for AdTech Data"
Variable Importance brings interpretability to popular black box modeling techniques. In this talk we study performance of popular ensemble techniques like Random Forest, Gradient Boosting with GLM. We observe certain traits that get magnified by non-linear techniques like Deep Learning that are otherwise missed by GBM or Random Forest.
We describe Open Source Scalable Machine Learning package, H2O which through ease-of-use and speed makes comparisons and picking best-of-breed and ensembles more natural. H2O's implementation of these algorithms tracks popular open source and text book implementations closely.
Read: issuu.com/shuweigoh/docs/skymind
At Skymind, we’re tackling some of the most advanced problems in data analysis and machine intelligence. We offer state-of-the-art, flexible, scalable deep learning for industry. Deep learning is becoming an important tool set for natural-language processing (NLP), computer vision, database predictions, pattern recognition, image/video processing and fraud detection.
Conceptual understanding of how credit card processing can be enabled in SAP. The slides explain the basic steps which need to be undertaken so that the credit card orders can be processed.
Введение в архитектуры нейронных сетей / HighLoad++ 2016Grigory Sapunov
Slides from HighLoad++ 2016 conference.
Introduction into neural network architectures (Rus)
Презентация для конференции HighLoad++ 2016.
http://www.highload.ru/2016/abstracts/2454.html
Видеозапись доклада:
https://www.youtube.com/watch?v=XY5AczPW7V4
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
Conceptual understanding of how credit card processing can be enabled in SAP. The slides explain the basic steps which need to be undertaken so that the credit card orders can be processed.
Введение в архитектуры нейронных сетей / HighLoad++ 2016Grigory Sapunov
Slides from HighLoad++ 2016 conference.
Introduction into neural network architectures (Rus)
Презентация для конференции HighLoad++ 2016.
http://www.highload.ru/2016/abstracts/2454.html
Видеозапись доклада:
https://www.youtube.com/watch?v=XY5AczPW7V4
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
A few slides from my "investor spotlight" talk at BioCap 2023 at Alderley Park on 28/09/2023, shining a light on some things that improve a biotech startup's chances of successfully raising funding even in difficult times.
The Rise of the Machines: Understanding How Data Accelerates AIVolker Hirsch
These are my slides to a keynote I gave at the annual conference of the Association of Learned and Professional Society Publishers (ALPSP) in Noordwijk, Netherlands on 15 September 2017. They look at how data (and the increase of data sources we create) helps accelerate the power of AI.
They didn't shoot video but the audio is here: https://www.alpsp.org/write/MediaUploads/Conference/1709AIC/Audio/Plenary_5_-_Volker_Hirsch.mp3
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
AI & The Future of Work - Work & Life in the Age of RobotsVolker Hirsch
The slides to my keynote at the annual conference for the Association of Business Psychology (ABP), held in London on 14 Oct 2016. It's a "shock & awe" take on what's coming and why we need to be alert to those changes.
"What Have You Done Tomorrow" @ HR Vision Amsterdam 2015Volker Hirsch
The slides to my keynote delivered at the opening dinner of the HR Vision Amsterdam 2015 conference. I am dealing with the rapid change societies around the world will face with the ascent of faster computing, AI and robotics. Not only dystopian, I also offer thoughts about some pathways to look at for humanity to start making the most of this.
These are the slides to my keynote on "Mobile Learning - Done Right", delivered at the Exec I/O Mobile event of the European Pirate Summit in Cologne on 5 September 2014.
Finding Money - 4YFN, Barcelona, 25 Feb 2014Volker Hirsch
The slides to my talk "Finding Money" delivered at 4YFN ("Four Years From Now"), the GSMA's Entrepreneurship Conference in Barcelona as part of the Mobile World Congress.
Capturing Users / Using social, engagement and mobile to drive acquisition an...Volker Hirsch
The slides to my talk at StartUp Next Sofia (which I also gave at the LauncHub Long Weekend) - delivered on 29 and 30 November 2013 in Sofia, Bulgaria.
The slides to my little talk at TIGA GameDev Night in Leeds (6 Sept 2012). It is sort of an abbreviated version of some I did previously (plus pretty Pegasus)
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
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