A noble approach to help poor students in isolated areas while reaping benefit. This is to share with you all that we are implementing the following strategy to assist a greater number of disadvantaged and talented students from remote places in turning their potential skills into success stories. Along with the needy students, this approach will also benefit the organizations with skilled and dedicated resources in addition to the students. Implementation strategy: 1. For more than 20 years, COMET bas been providing FREE training to B.Tech and BCA students, in all tools and technologies needed in IT industry. 2. As the number of students taking COMET help continues to increase, we are partnering with with organizations who are interested in providing these students with additional training in tools and technologies related to their line of work. 3. The partnering organizations provides these COMET trained students with additional training and high quality assignments that students aim to complete them within allotted time frame. 4. Post these additional training, When the aiding organization needs resources, they can evaluate these students and hire best candidates. If any of you would like to support COMET foundation here and gain access to skilled resources, let us know. +91 9949 9949 89 | +91 98108 25306
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🌐 "STARC: A General Framework For Quantifying Differences Between Reward Functions" has been accepted at #ICLR! STARC quantifies differences in reward functions, enhancing analysis and safety of reward learning algorithms in reinforcement learning. Key Highlights: 🔑 Robust Framework: STARC metrics establish a comprehensive standard for evaluating reward functions by providing both upper and lower bounds on worst-case regret. 📘 Theoretical and Practical Validation: Validates both soundness and completeness, ensuring reliability in diverse experimental conditions. ⚖️ Enhanced Comparability: Improves comparisons between reward learning algorithms, offering consistent results across varied environments. 📊 Empirical Superiority: Demonstrated to surpass existing methods in aligning with worst-case regret, verifying its utility in real-world applications. 📄Read the full paper: https://lnkd.in/g_9grswT Congrats to the authors: Joar Max Viktor Skalse, Lucy Farnik, Sumeet Motwani, Erik Jenner, Adam Gleave, Alessandro Abate!
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Reward functions for robotics can be specified in natural language zero-shot using vision-language models. Promising way of leveraging foundation models to align systems in a wider variety of domains.
🌐 "STARC: A General Framework For Quantifying Differences Between Reward Functions" has been accepted at #ICLR! STARC quantifies differences in reward functions, enhancing analysis and safety of reward learning algorithms in reinforcement learning. Key Highlights: 🔑 Robust Framework: STARC metrics establish a comprehensive standard for evaluating reward functions by providing both upper and lower bounds on worst-case regret. 📘 Theoretical and Practical Validation: Validates both soundness and completeness, ensuring reliability in diverse experimental conditions. ⚖️ Enhanced Comparability: Improves comparisons between reward learning algorithms, offering consistent results across varied environments. 📊 Empirical Superiority: Demonstrated to surpass existing methods in aligning with worst-case regret, verifying its utility in real-world applications. 📄Read the full paper: https://lnkd.in/g_9grswT Congrats to the authors: Joar Max Viktor Skalse, Lucy Farnik, Sumeet Motwani, Erik Jenner, Adam Gleave, Alessandro Abate!
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#Day 119 #DSA . #251 Days challenge. # Day 119, Today i solve leetcode POTD question that is All Ancestors of a Node in a Directed Acyclic Graph. 🚀 Today, we lay the foundation for an incredible journey ahead. Dive into the basics, explore key concepts, and set the stage for success. Remember, every small step counts towards greatness. Let's make today count! 💪 #Day119 #DSA #251DayChallenge
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🎉 Exciting Update! 🎉 Day 4 at RSL Solutions: Dived into the fascinating world of recursion today! 🔄💡 Key Takeaways: 🔍 Explored the power of recursion in problem-solving. 🔢 Solved several challenging problems using recursive functions. 🧠 Mastered the concept of recursive calls and base cases. 🌟 Tackled problems with increased confidence and efficiency! 💻 Excited for more learning adventures and problem-solving triumphs at RSL! 🚀🌐 #RSLsolutions #RecursionRocks #ProblemSolvingPro
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🚀 Day 4 of my #DSA Journey: Understanding Time Complexity! 🧠 📊 Time complexity is all about understanding how algorithms scale with input size. Whether it's O(1) (constant time) or O(n²) (quadratic time), knowing the difference helps in choosing the most efficient solution. Here's a quick breakdown: 💡 Linear Time (O(n)) – Looping through an array? The time grows with the size! 💡 Quadratic Time (O(n²)) – Nested loops mean the time can grow rapidly. The worst-case scenario? Always be prepared for the longest possible execution time! 💪 #DSA #TimeComplexity #ProblemSolving #CodingJourney #100DaysOfCode #MERN
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Ready for a brain teaser? 🤓 We bet you wont get this riddle without Googling it! Share your guesses in the comments below, and we’ll tell you if you’re right! If you guessed correctly… Share this post with a friend to see if they can guess it! 💬 . #EleionProfessionalGroup #RiddleoftheDay #YEG #YEGCPA
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Aspiring MERN Stack Developer | Expertise in Node.js, Express.js, React.js & MongoDB | Strong Foundation in Java & Data Structures
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Author, Founder, CEO @ Moneil | MBA, B.Engg-Helping Busy Entrepreneurs to build Wealth through Real Estate, AUM $1Billion+, 3x Top Author Active & Passive Investing Expert, Philanthropist, Positivity & Happiness Mentor
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📚 Day 98 of 100 Days of Code Challenge 🚀 Today's Learning Highlights: 1. Learned about Topic Modelling. 2. Basic theory of how Latent Dirichlet Allocation works. 3. Basic theory of Non-negative Matrix Factorization. 4. Also conducted the gdsc event. #100daysofcode #100daysofcodechallenge #dl #deeplearning
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