Jun 11
📢 Newly published research in #mdpisystems 👉mdpi.com/2079-8954/14/5/508
Automated Inference of Systems Capability from Natural Language Artifacts
by authors from University of South-Eastern Norway
#NaturalLanguageProcessing #EnterpriseSystemsEngineering #FeatureLearning
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16 Jul 2025
I’m pleased to share a detailed blog on Feature-based vs. GAN-based Learning from Demonstrations: When and Why.
This article provides valuable insights for those looking to scale RL systems using offline reference data, especially in the context of physics-based agent control.
🔗 Read it here: breadli428.github.io/post/lf…
📄 Full paper: arxiv.org/pdf/2507.05906
Great work by @breadli428!
#ReinforcementLearning #ImitationLearning #AI #Robotics #LFD #GAN #FeatureLearning #RL #DL
15 Jul 2025
🧠With the shift in humanoid control from pure RL to learning from demonstrations, we take a step back to unpack the landscape.
🔗breadli428.github.io/post/lf…
🚀Excited to share our blog post on Feature-based vs. GAN-based Learning from Demonstrations—when to use which, and why it matters.
✅We hope this serves as a guide for anyone looking to scale their RL systems using offline reference data, paving the way toward leveraging large-scale demonstrations.
📄PDF: arxiv.org/pdf/2507.05906
🙌Massive thanks to @frankzydou, @TairanHe99, @xuxin_cheng, @zhengyiluo, and @ChenTessler for their invaluable input in shaping the final form of this work!
#ai #robotics #humanoids #machine_learning #reinforcement_learning #learning_from_demonstrations #representation_learning #computer_graphics #computer_science
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22 Apr 2025
✈️🇸🇬 to #ICLR 2025 🔥🔥🔥 at the iconic city of #Singapore participating in The Thirteenth International Conference on Learning Representations, one of the 4 main #machinelearning #ai conferences worldwide, with Dr @josesanchezhb
This year with promissing Invited talks by @dawnsongtweets Song-Chun Zhu @danqi_chen @zicokolter @YiMaTweets @_rockt and 44 workshops, 3827 papers, orals, posters, socials and many more, featuring @SchmidhuberAI @SLapuschkin @lifu_huang @Yoshua_Bengio @sea_snell @wellingmax @svlevine @pabbeel to name a very limited few
Thanks to the ICLR organizers: @yisongyue @cvondrick @yuqirose @animesh_garg @orussakovsk
@pcastr @francescazfl @savvyRL @fredahshi @SchwinnLeo Jonas Köhler and many others, including the 10s of sponsors like: @Microsoft @AIatMeta @Google @amazon @Oracle @Huawei @Apple @UnitreeRobotics and many others for making it possible one more year.
See you all!
PD: We will be hosting two @_Qubic_ AGI dinners on the 24th & 25th seats are very limited but DM if you are interested
#Artificialntelligence #AI #AGI #RepresentationLearning #FeatureLearning #UnsupervisedLearning #SemiSupervisedLearning #SupervisedLearning #MetricLearning #KernelLearning #SparseCoding #DimensionalityExpansion #HierarchicalModels #OptimalTransport #DeepLearningTheory #Planning #ReinforcementLearning #ComputerVision #NLP #AudioProcessing #SpeechRecognition #Robotics #Neuroscience #Biology #ClimateScience #Sustainability #Fairness #AIethics #Safety #Privacy #Interpretability #ExplainableAI #Visualization #Optimization #TheEndOfKnowledge #Artificiology
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🤗Let's join us to learn how machines can automatically detect and classify features using feature learning in machine learning. Discover more about representation learning and its variants here: blog.ai.aioz.io/guides/compu…
#machinelearning #featurelearning #AI
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Retrieving subjectively interesting sounds from electronic music tracks in: “Searching For Loops And Sound Samples With #FeatureLearning” by Jan Jakubik. @FedCSIS 2022 publication, ACSIS Vol. 31 p. 13-18; open access: tinyurl.com/yc4keyy5
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21 Jul 2022
Saved this Tweet to your Notion database.
Tags: [Ml, Covid19, Featurelearning, Dl]
17 May 2022
Investigators from @UCSF_ci2, @UCSF_Epibiostat, @UCSFCDHI, @UCSFImaging applied data-driven #FeatureLearning to classify the presence and 8-years incidence of #KneePain from #MRI. Read about it via @OACJournal ➡️ bit.ly/3Mr0M57
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16 Dec 2021
#DeepLearning on #PointClouds and Its Application: A Survey @WHUni_Official @UCAS1978
👉mdpi.com/1424-8220/19/19/418…
#RemoteSensors #sensors #FeatureLearning
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13 Oct 2021
📢 Join us 10/15 4pm CT for "On #FeatureLearning in #NeuralNetworks: Emergence from Inputs & Advantage over Fixed Features" @WisconsinCS' Yingyu Liang will show that #GradientDescent-trained networks can succeed on #LearningProblems. Online talk deets: bit.ly/liang_10_15 #ML
ALT University of Iowa Computer Science Colloquium Series On Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features Yingyu Liang Friday, October 15, 2021 4:00 p.m. CT Abstract and more at cs.uiowa.edu Yingyu Liang is an assistant professor at the University of Wisconsin-Madison, with research interests in optimization and generalization of deep learning, robust machine learning, and their applications. He received a Ph.D. degree in CS from Georgia Tech in 2014 and was a postdoc in 2014-17 in CS at Princeton University. He received the NSF Career Award in 2021.
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13 Jun 2021
Interested in #multimodal #RemoteSensing?
Check our recent work @Zhu_XLab on Multimodal remote sensing #benchmark #datasets for #LandCover classification with a shared and specific #FeatureLearning model.
Paper @isprs : authors.elsevier.com/sd/arti…
Datasets: github.com/danfenghong/ISPRS…
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2 Apr 2021
#DeepLearning on #PointClouds and Its Application: A Survey
👉mdpi.com/1424-8220/19/19/418…
#featurelearning
#applicationofpointcloud
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20 Nov 2020
In this article, by @nielspace07, you will find everything you need to know about #Representation and #FeatureLearning – check it out!
bit.ly/3pKwGhW
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25 Mar 2020
HARVESTMAN: A framework for hierarchical featurelearning and selection from whole genome sequencingdata biorxiv.org/cgi/content/shor… #biorxiv_bioinfo
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25 Mar 2020
HARVESTMAN: A framework for hierarchical featurelearning and selection from whole genome sequencingdata biorxiv.org/cgi/content/shor… #bioRxiv
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28 Oct 2019
We are presenting six @intelsensing papers this week at #iccv2019 (see the list below). If you are interested in #ActiveLearning #ZeroShotLearning #FeatureLearning #NovelViewVideoSynthesis #ReinforcementLearning come and talk with us!
#MachineLearning #AI #ComputerVision
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22 Aug 2019
Excited about some of the recent work on dictionary learning from tensor data that M. Ghassemi, Z. Shakeri, @ergodicwalk, and myself have been doing; here is a recent paper (shorter ISIT 2019 version): inspirelab.us/wp-content/upl… #DictionaryLearning #FeatureLearning
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12 Oct 2018
Day 087, #100DaysOfMLCode. Studied more about feature learning, also wrote a medium article about the differences between analog and digital sound and different challenges that come up in each media #music #featurelearning
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11 Oct 2018
Day 086, #100DaysOfMLCode. Studied more about feature learning, also wrote a medium article about Envelope And Spectrogram #music #featurelearning
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資料を公開しました。関西CV・PRML勉強会（goo.gl/pMu9A2）で発表します。
解説論文『Unsupervised Featurelearning Via Non-Parametric Instance-Level Discrimination』
【キーワード】
・Unsupervised Learning
・Feature Learning
slideshare.net/yamatookamoto…
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9 Oct 2017
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