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With the rise of artificial intelligence in use today including applications like Siri, Alexa, Tesla, Cortana, Cogito, Google Now, and even Netflix, podcasts are a great alternative to keep yourself updated. We've gathered a list of podcasts available for you about this technology where you can get the latest news and trends plus learn more about how AI works and its impact on our lives.
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Sam Charrington

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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, de ...
 
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AI with AI: Artificial Intelligence with Andy Ilachinski

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AI with AI: Artificial Intelligence with Andy Ilachinski

CNA

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AI with AI explores the latest breakthroughs in artificial intelligence and autonomy, and discusses the technological and military implications. Join Andy Ilachinski and David Broyles as they explain the latest developments in this rapidly evolving field. The views expressed here are those of the commentators and do not necessarily reflect the views of CNA or any of its sponsors.
 
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Conversations on Applied AI - Stories from Experts in Artificial Intelligence

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Conversations on Applied AI - Stories from Experts in Artificial Intelligence

Justin Grammens

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Welcome to the Conversations on Applied AI Podcast where Justin Grammens and the team at Emerging Technologies North talk with experts in the fields of Artificial Intelligence and Deep Learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real-world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at AppliedAI.MN. Enjoy!
 
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Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

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Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Felipe Flores

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Artificial intelligence is a tremendously beneficial technology that's advancing at an incredibly rapid pace. As more and more organisations adopt and implement AI we find that the main challenges are not in the technology itself but in the human side, ie: the approaches, chosen problems and what's called 'the last mile', etc. That's why Data Futurology focuses on the leadership side of AI and how to get the most value from it. Join me, Felipe Flores, a Data Science executive with almost 20 ...
 
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Artificial Intelligence on the Battlefield

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Artificial Intelligence on the Battlefield

Shephard Media

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Artificial intelligence technologies are undoubtedly beginning to change the face of modern warfare.AI and machine learning applications promise to enhance productivity, reduce user workload, and operate more quickly than humans. But, this doesn’t come without its challenges. The Artificial Intelligence on the Battlefield podcast dives into these issues and more, looking at just how will Artificial Intelligence reshape the future of warfare? Created by Shephard Studio, The Artificial Intelli ...
 
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HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

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HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

David Yakobovitch

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David Yakobovitch explores AI for consumers through fireside conversations with industry thought leaders on HumAIn. From Chief Data Scientists and AI Advisors, to Leaders who advance AI for All, the HumAIn Podcast is the channel to release new AI products, to learn about industry trends, and to bridge the gap between humans and machines in the Fourth Industrial Revolution.
 
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Artificial Intelligence II 2018 (QHD 1920 - Video & Folien)

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Artificial Intelligence II 2018 (QHD 1920 - Video & Folien)

Prof. Dr. Michael Kohlhase

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This course covers the foundations of Artificial Intelligence (AI), in particular reasoning under uncertainty, machine learning and (if there is time) natural language understanding. This course builds on the course Artificial Intelligence I from the preceding winter semester and continues it Learning Goals and Competencies Technical, Learning, and Method Competencies Knowledge: The students learn foundational representations and algorithms in AI. Application: The concepts learned are applie ...
 
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MIT 6.034 Artificial Intelligence, Fall 2010

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MIT 6.034 Artificial Intelligence, Fall 2010

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View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
 
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Artificial Intelligence

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Artificial Intelligence

Kristina Kent & Mary MacLeod

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The world’s brightest minds are working tirelessly to harness the power of ai in order to gain a deeper understanding of life, existence, and also subsequently being... Well they can stop right now, because Mary and Tina have the answers. The girls have put in the work; minutes of research have culminated in this definitive resource for life’s biggest questions.
 
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Artificial Intelligence in Drug Discovery

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Artificial Intelligence in Drug Discovery

Simon Smith

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Get knowledge and inspiration to apply artificial intelligence to drug development. Discover startups applying machine learning to biomedical research. Hear how biotech and pharma companies use AI to speed discovery and cut costs. Learn from academic researchers pushing boundaries in applying computation to biology. We interview leaders transforming drug development with data and algorithms. Subscribe now and never miss an episode!
 
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Talking Robots - The Podcast on Robotics and Artificial Intelligence

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Talking Robots - The Podcast on Robotics and Artificial Intelligence

Laboratory of Intelligent Systems

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Talking Robots is a podcast featuring interviews with high-profile professionals in Robotics and Artificial Intelligence for an inside view on the science, technology, and business of intelligent robotics. It is managed and sponsored by the Laboratory of Intelligent Systems (LIS) at the EPFL in Lausanne, Switzerland.
 
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Awakened Humanity Podcast - Artificial Intelligence | Human Intelligence | Consciousness

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Awakened Humanity Podcast - Artificial Intelligence | Human Intelligence | Consciousness

Lolita Aufmuth | Founder Vision United World

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The Awakened Humanity Podcast is your Podcast for artificial and human intelligence. You can expect a wide mix of inspiring interviews with top international experts and updates on current developments in these areas. Are we driven by technology or do we drive it? How can we find a balance between ethics and technology? What does it mean to be a human being in the AI age? The Awakened Humanity Podcast is all about asking deep questions and providing you with information and inspiration about ...
 
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A Word On Artificial Intelligence (A.I.)

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A Word On Artificial Intelligence (A.I.)

Primedia Broadcasting

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An introduction to machine learning to assist business leaders to understand what it can and can't do. In the three episodes, you will get a sense of the potential impact, the nature and types of models available and case studies that may apply to your industry. Allan Kent is the Head of Digital at Primedia Broadcasting and is the host of this series.
 
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Applied Artificial Intelligence For Business

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Applied Artificial Intelligence For Business

TOPBOTS

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TOPBOTS educates business leaders on high-impact applications of modern machine learning and AI techniques and helps leading organizations adopt and implement emerging technologies. We run the largest publication and community for enterprise AI professionals to learn about the latest machine learning and automation solutions and exchange insights with each other. Through education and community, we inspire you to think creatively about how AI can be used to improve lives, revolutionize indus ...
 
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Courageous! Artificial Intelligence & Cybersecurity Think Tank with Dr. Rollan Roberts

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Courageous! Artificial Intelligence & Cybersecurity Think Tank with Dr. Rollan Roberts

Dr. Rollan Roberts II

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Dr. Rollan Roberts is an advisor and resource to national governments on strong Artificial Intelligence and quantum-proof Cybersecurity and was nominated to Central Command's Department of Defense Civilian Task Force. He is the CEO of Courageous!, a superhuman AI and Cybersecurity research and product development think tank that serves advanced national security initiatives of national governments. He served as CEO of the Hoverboard company, creating the best-selling consumer product worldwi ...
 
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The Artificial Podcast

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The Artificial Podcast

RedFox AI

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The Artificial Podcast is a weekly podcast series that breaks down trending topics in the world of emerging technology. Our goal is to help everyone understand how emerging technologies like Artificial Intelligence, Voice, Blockchain, IoT, Robotics, Space Tech, and many more are impacting our lives each and every day. We also interview emerging technology leaders and experts from around the world who are actively working with these technologies to ensure that they help us make life better fo ...
 
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AI with AI: Artificial Intelligence with Andy Ilachinski

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Chasing AIMe 35:33

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Andy and Dave discuss the latest in AI news and research, including: [1:28] Researchers from several universities in biomedicine establish the AIMe registry, a community-driven reporting platform for providing information and standards of AI research in biomedicine. [4:15] Reuters publishes a report with insight into examples at Google, Microsoft, …
 
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Artificial Intelligence: Paper Time

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Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation

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Jay Patravali, Gaurav Mittal, Ye Yu, Fuxin Li, Mei ChenAbstractWe present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D and 3D CNN architecture via contrastive learning to capture the appearance-specific spatial and action-spec…
 
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523 40:41

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Today we’re joined by a friend of the show and return guest Ville Tuulos, CEO and co-founder of Outerbounds. In our previous conversations with Ville, we explored his experience building and deploying the open-source framework, Metaflow, while working at Netflix. Since our last chat, Ville has embarked on a few new journeys, including writing the u…
 
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AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

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AI Today podcast: AI Education Series: Thinking and Acting Like a Data Scientist 25:54

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This podcast episode provides a snippet of Cognilytica’s AI and ML education from our Cognilytica Education Subscription. There are many paths to data science and different skills are needed for creating successful data scientists. These skills include curiosity, analytical thinking, performing analysis, taking a strong position and arguing that po…
 
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HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education

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Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic 38:07

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Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Alex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehe…
 
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Conversations on Applied AI - Stories from Experts in Artificial Intelligence

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Ryan Phelan - The Impact of AI on Patents and Intellectual Property 45:43

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The conversation this week is all about the intersection of Intellectual Property and Artificial Intelligence! I'm thrilled to have an expert in the fields of Artificial Intelligence, Patents, and Intellectual Property on the program. This is an amazing conversation and one in which I learned so much in just a short period of time from our guest, R…
 
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Artificial Intelligence: Paper Time

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Scalable Online Planning via Reinforcement Learning Fine-Tuning

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Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, Noam BrownAbstractLookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many other settings, are tabular. Tabular search methods do not scale well with the size of the se…
 
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Artificial Intelligence: Paper Time

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Width-Based Planning and Active Learning for Atari

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Benjamin Ayton, Masataro AsaiAbstractWidth-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent width-based approaches have computed feature vectors for each screen using a hand designed feature set or a variational autoencoder …
 
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Artificial Intelligence: Paper Time

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Analyzing Human Models that Adapt Online

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Andrea Bajcsy, Anand Siththaranjan, Claire J. Tomlin, Anca D. DraganAbstractPredictive human models often need to adapt their parameters online from human data. This raises previously ignored safety-related questions for robots relying on these models such as what the model could learn online and how quickly could it learn it. For instance, when wi…
 
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Artificial Intelligence: Paper Time

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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus

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Jesse Dodge, Maarten Sap, Ana Marasovi\'c, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, Matt GardnerAbstractLarge language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant port…
 
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Artificial Intelligence: Paper Time

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Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance

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Raphael Trumpp, Harald Bayerlein and David GesbertAbstractReliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The sequential nature of the vehicle-pedestrian interaction, i.e., where immediate decisions of one agent directly influence the following decisions of the other agent, is …
 
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Artificial Intelligence: Paper Time

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Feature Selection for Multivariate Time Series via Network Pruning

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Kang Gu, Soroush Vosoughi, Temiloluwa PrioleauAbstractIn recent years, there has been an ever increasing amount of multivariate time series (MTS) data in various domains, typically generated by a large family of sensors such as wearable devices. This has led to the development of novel learning methods on MTS data, with deep learning models dominat…
 
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Artificial Intelligence: Paper Time

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Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks

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Yichen Jiang, Mohit BansalAbstractSystematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions. However, existing neural models have been shown to lack this basic ability in learning symbolic structures. Motivated by the failure of a Transformer model on the SCAN comp…
 
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Artificial Intelligence: Paper Time

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Multi Scale Graph Wavenet for Spatio-temporal Wind Speed Forecasting

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Neetesh Rathore, Pradeep Rathore, Arghya Basak, Sri Harsha Nistala, Venkataramana RunkanaAbstractGeometric deep learning has gained tremendous attention in both academia and industry due to its inherent capability of representing arbitrary structures. Due to exponential increase in interest towards renewable sources of energy, especially wind energ…
 
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Artificial Intelligence: Paper Time

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GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

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Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing XieAbstractThe representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Recent breakthroughs on pretrained language models a…
 
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Artificial Intelligence: Paper Time

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Multilingual AMR Parsing with Noisy Knowledge Distillation

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Deng Cai and Xin Li and Jackie Chun-Sing Ho and Lidong Bing and Wai LamAbstractWe study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher. We constrain our exploration in a strict multilingual setting: there is b…
 
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Artificial Intelligence: Paper Time

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Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising

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Shaojie Tang, Jing YuanAbstractThe idea of social advertising (or social promotion) is to select a group of influential individuals (a.k.a \emph{seeds}) to help promote some products or ideas through an online social networks. There are two major players in the social advertising ecosystem: advertiser and platform. The platform sells viral engageme…
 
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Artificial Intelligence: Paper Time

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You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors

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Zijian Zhu, Hang Su, Chang Liu, Wenzhao Xiang and Shibao ZhengAbstractBlind spots or outright deceit can bedevil and deceive machine learning models. Unidentified objects such as digital "stickers," also known as adversarial patches, can fool facial recognition systems, surveillance systems and self-driving cars. Fortunately, most existing adversar…
 
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Artificial Intelligence: Paper Time

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Focused Contrastive Training for Test-based Constituency Analysis

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Benjamin Roth, Erion \c{C}anoAbstractWe propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation th…
 
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Artificial Intelligence: Paper Time

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Reinforcement Learning with Information-Theoretic Actuation

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Elliot Catt, Marcus Hutter, Joel VenessAbstractReinforcement Learning formalises an embodied agent's interaction with the environment through observations, rewards and actions. But where do the actions come from? Actions are often considered to represent something external, such as the movement of a limb, a chess piece, or more generally, the outpu…
 
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Artificial Intelligence: Paper Time

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A Review of Text Style Transfer using Deep Learning

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Martina Toshevska, Sonja GievskaAbstractStyle is an integral component of a sentence indicated by the choice of words a person makes. Different people have different ways of expressing themselves, however, they adjust their speaking and writing style to a social context, an audience, an interlocutor or the formality of an occasion. Text style trans…
 
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Artificial Intelligence: Paper Time

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CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models

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Minwoo Lee, Seungpil Won, Juae Kim, Hwanhee Lee, Cheoneum Park, Kyomin JungAbstractFact verification datasets are typically constructed using crowdsourcing techniques due to the lack of text sources with veracity labels. However, the crowdsourcing process often produces undesired biases in data that cause models to learn spurious patterns. In this …
 
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Artificial Intelligence: Paper Time

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Scalable Rule-Based Representation Learning for Interpretable Classification

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Zhuo Wang, Wei Zhang, Ning Liu, Jianyong WangAbstractRule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and stru…
 
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Artificial Intelligence: Paper Time

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Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks

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Andreas Wachter and Werner NahmAbstractSupervised training of neural networks requires large, diverse and well annotated data sets. In the medical field, this is often difficult to achieve due to constraints in time, expert knowledge and prevalence of an event. Artificial data augmentation can help to prevent overfitting and improve the detection o…
 
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Artificial Intelligence: Paper Time

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Does My Representation Capture X? Probe-Ably

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Deborah Ferreira, Julia Rozanova, Mokanarangan Thayaparan, Marco Valentino, Andr\'e FreitasAbstractProbing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. Probing studies may have misleading results, but various recent wor…
 
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Artificial Intelligence: Paper Time

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USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence

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Jing Yao, Zhicheng Dou, Ruobing Xie, Yanxiong Lu, Zhiping Wang, Ji-Rong WenAbstractSearch and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and r…
 
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Artificial Intelligence: Paper Time

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Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning

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Eshagh Kargar and Ville KyrkiAbstractDriving in a dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision-making policy. The learning of such a policy requires a state representation that can encode the entire environment. Mid-level representations that encode a vehicle's environment as images have becom…
 
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Artificial Intelligence: Paper Time

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A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning

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Yimin ShiAbstractDeep Reinforcement Learning (DRL) sometimes needs a large amount of data to converge in the training procedure and in some cases, each action of the agent may produce regret. This barrier naturally motivates different data sets or environment owners to cooperate to share their knowledge and train their agents more efficiently. Howe…
 
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Artificial Intelligence: Paper Time

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The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition

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Christophe De Wagter and Federico Paredes-Vall\'es and Nilay Sheth and Guido de CroonAbstractRobotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space with inherent real-time requirements. One extreme challenge in robotics is curre…
 
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Artificial Intelligence: Paper Time

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Moving Object Detection for Event-based vision using Graph Spectral Clustering

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Anindya Mondal, Shashant R, Jhony H. Giraldo, Thierry Bouwmans, Ananda S. ChowdhuryAbstractMoving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimi…
 
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Artificial Intelligence: Paper Time

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Crowdsourcing through Cognitive Opportunistic Networks

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M. Mordacchini, A. Passarella, M. Conti, S.M. Allen, M.J. Chorley, G.B. Colombo, V. Tanasescu and R.M. WhitakerAbstractUntile recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However the emergence of opportunistic networking (ON) has opened up crowdsourcing to the spatial domain. In…
 
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Artificial Intelligence: Paper Time

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CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations

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Mohammadreza Zolfaghari, Yi Zhu, Peter Gehler, Thomas BroxAbstractContrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn cross-modal embeddings for video and text, yet without exploiting its full potential. In particular, prev…
 
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Artificial Intelligence: Paper Time

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Accelerating Fully Connected Neural Network on Optical Network-on-Chip (ONoC)

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Fei Dai, Yawen Chen, Haibo Zhang, and Zhiyi HuangAbstractFully Connected Neural Network (FCNN) is a class of Artificial Neural Networks widely used in computer science and engineering, whereas the training process can take a long time with large datasets in existing many-core systems. Optical Network-on-Chip (ONoC), an emerging chip-scale optical i…
 
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Artificial Intelligence: Paper Time

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Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks

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Turker Ince, Junaid Malik, Ozer Can Devecioglu, Serkan Kiranyaz, Onur Avci, Levent Eren and Moncef GabboujAbstractPreventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine learning monitoring…
 
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Artificial Intelligence: Paper Time

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First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data

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Masataka Tasumi, Kazuki Iwahana, Naoto Yanai, Katsunari Shishido, Toshiya Shimizu, Yuji Higuchi, Ikuya Morikawa, Jun YajimaAbstractModel extraction attacks are a kind of attacks where an adversary obtains a machine learning model whose performance is comparable with one of the victim model through queries and their results. This paper presents a no…
 
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Artificial Intelligence: Paper Time

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A Survey of Embodied AI: From Simulators to Research Tasks

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Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu and Cheston TanAbstractThere has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with their environments…
 
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Artificial Intelligence: Paper Time

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LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values

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Zhao-Yu Zhang, Shao-Qun Zhang, Yuan Jiang, and Zhi-Hua ZhouAbstractMultivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values. In recent years, there has been an increasing interest in using end-to-end models to handle MTS with missing values. To generate features for prediction, existin…
 
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Artificial Intelligence: Paper Time

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Integrating Graph Contextualized Knowledge into Pre-trained Language Models

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Bin He, Di Zhou, Jinghui Xiao, Xin jiang, Qun Liu, Nicholas Jing Yuan, Tong XuAbstractComplex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information. However, traditional methods usually treat a triple as a training unit during the knowledge representation learning (KRL) procedure, neglectin…
 
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Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

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Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael KatzAbstractRecent advances in reinforcement learning (RL) have led to a growing interest in applying RL to classical planning domains or applying classical planning methods to some complex RL domains. However, the long-horizon goal-based probl…
 
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Artificial Intelligence: Paper Time

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Twins: Revisiting the Design of Spatial Attention in Vision Transformers

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Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua ShenAbstractVery recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we re…
 
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Artificial Intelligence: Paper Time

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Introducing the DOME Activation Functions

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Mohamed E. Hussein and Wael AbdAlmageedAbstractIn this paper, we introduce a novel non-linear activation function that spontaneously induces class-compactness and regularization in the embedding space of neural networks. The function is dubbed DOME for Difference Of Mirrored Exponential terms. The basic form of the function can replace the sigmoid …
 
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Artificial Intelligence: Paper Time

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Emergency Vehicles Audio Detection and Localization in AutonomousDriving

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Hongyi Sun, Xinyi Liu, Kecheng Xu, Jinghao Miao, Qi LuoAbstractEmergency vehicles in service have right-of-way over all other vehicles. Hence, all other vehicles are supposed to take proper actions to yield emergency vehicles with active sirens. As this task requires the cooperation between ears and eyes for human drivers, it also needs audio detec…
 
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Artificial Intelligence: Paper Time

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Improvising the Learning of Neural Networks on Hyperspherical Manifold

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Lalith Bharadwaj Baru, Sai Vardhan Kanumolu, Akshay Patel ShilhoraAbstractThe impact of convolution neural networks (CNNs) in the supervised settings provided tremendous increment in performance. The representations learned from CNN's operated on hyperspherical manifold led to insightful outcomes in face recognition, face identification and other s…
 
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Artificial Intelligence: Paper Time

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Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

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Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao, Kristian Muri Knausg{\aa}rd, Angela Helen Martin, Marta Moyano, Rebekah A. Oomen, Jeppe Have Rasmussen, Tonje Knutsen S{\o}rdalen, Susanna Huneide Thorbj{\o}rnsenAbstractThe deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power o…
 
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Artificial Intelligence: Paper Time

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Batched Bandits with Crowd Externalities

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Romain Laroche, Othmane Safsafi, Raphael Feraud, Nicolas BroutinAbstractIn Batched Multi-Armed Bandits (BMAB), the policy is not allowed to be updated at each time step. Usually, the setting asserts a maximum number of allowed policy updates and the algorithm schedules them so that to minimize the expected regret. In this paper, we describe a novel…
 
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Artificial Intelligence: Paper Time

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The MatrixX Solver For Argumentation Frameworks

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Maximilian HeinrichAbstractMatrixX is a solver for Abstract Argumentation Frameworks. Offensive and defensive properties of an Argumentation Framework are notated in a matrix style. Rows and columns of this matrix are systematically reduced by the solver. This procedure is implemented through the use of hash maps in order to accelerate calculation …
 
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Artificial Intelligence: Paper Time

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Collaborative Storytelling with Human Actors and AI Narrators

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Boyd Branch, Piotr Mirowski, Kory W. MathewsonAbstractLarge language models can be used for collaborative storytelling. In this work we report on using GPT-3 \cite{brown2020language} to co-narrate stories. The AI system must track plot progression and character arcs while the human actors perform scenes. This event report details how a novel conver…
 
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Dr Jekyll and Mr Hyde: the Strange Case of Off-Policy Policy Updates

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Romain Laroche, Remi TachetAbstractThe policy gradient theorem states that the policy should only be updated in states that are visited by the current policy, which leads to insufficient planning in the off-policy states, and thus to convergence to suboptimal policies. We tackle this planning issue by extending the policy gradient theory to policy …
 
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Artificial Intelligence: Paper Time

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Explanation-Aware Experience Replay in Rule-Dense Environments

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Francesco Sovrano, Alex Raymond and Amanda ProrokAbstractHuman environments are often regulated by explicit and complex rulesets. Integrating Reinforcement Learning (RL) agents into such environments motivates the development of learning mechanisms that perform well in rule-dense and exception-ridden environments such as autonomous driving on regul…
 
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Artificial Intelligence: Paper Time

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BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining

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Weizhe Hua, Yichi Zhang, Chuan Guo, Zhiru Zhang, G. Edward SuhAbstractNeural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common corruptions also introduce a large computational overhead, requiring as many as ten times the number of…
 
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