Ivan Matoshchuk

REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets Quick read: https://lnkd.in/gbVW6KWG Researchers from Cornell, Princeton, and Carnegie Mellon University introduce REBEL: REgression to RElative REward Based RL. This algorithm reduces the problem of policy optimization by regressing the relative rewards via direct policy parameterization between two completions to a prompt, enabling strikingly lightweight implementation. Theoretical analysis reveals REBEL as a foundation for RL algorithms like Natural Policy Gradient, matching top theoretical guarantees for convergence and sample efficiency. REBEL accommodates offline data and addresses intransitive preferences that are common in practice. The researchers adopt the Contextual Bandit formulation for RL, which is particularly relevant for models like LLMs and Diffusion Models due to deterministic transitions. Prompt-response pairs are considered with a reward function to measure response quality. The KL-constrained RL problem is formulated to fine-tune the policy according to rewards while adhering to a baseline policy. A closed-form solution to the relative entropy problem is derived from prior research work, allowing the reward to be expressed as a function of the policy. REBEL iteratively updates the policy based on a square loss objective, utilizing paired samples to approximate the partition function. This core REBEL objective aims to fit the relative rewards between response pairs, ultimately seeking to solve the KL-constrained RL problem. Paper: https://lnkd.in/gzd8j3iK #artificialintelligence #reinforcementlearning

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Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review Quick read: https://lnkd.in/gEuAfS8e Paper: https://lnkd.in/gsdVWcM8 #datascience #machinelearning #ai

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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Our recent research on Recurrent Explicit Duration Switching Linear Dynamical Systems now available on arXiv! Many AI/ML approaches aim at solving spatio-temporal data segmentation problems. Some of them aim at understanding the complex dynamics of the data. Regular AI/ML approaches usually focus on recorded data and try to construct a model that distinguish the spatio-temporal patterns in the recorded data in a learning process. Bayesian approaches treat the data as a sample from a complex multi-modal probability distribution and try to discover such distribution. This delivers some picture of the phenomena under study, not only a black-box solution. This usually makes also the model more resistant to noises in the data. In our research, we propose a novel model called Recurrent Explicit Duration Switching Linear Dynamical Systems (REDSLDS) that incorporates recurrent explicit duration variables into the rSLDS model. We also propose an inference and learning scheme that involves the use of Pólya-gamma augmentation. Please find more details on https://lnkd.in/dNx8Vd2i.

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Jin Wang and Zhengjie Miao delve into innovative techniques for addressing the challenges associated with table annotation, particularly focusing on column annotation tasks in web tables. Watchog improved semantic type detection, relation extraction, and column population while minimizing the reliance on large-scale annotated datasets, which are often difficult and costly to obtain. 📊🔍 Learn how Watchog leverages contrastive learning to tackle the challenges of table annotation. 👉 Dive into the article to learn: The significance of web tables in today's data-driven world. ⭐ How Watchog simplifies semantic type detection, relation extraction, and column population. ⭐ The power of contrastive learning in unlocking valuable insights from unlabeled table corpus. ⭐ Results showcasing Watchog's superiority over previous methods, even with limited labeled data. Don't miss our paper presentation at SIGMOD 2024, where Zhengjie Miao will unveil the secrets of Watchog on June 13th! 🗓️✨ Read the full article here: https://lnkd.in/gBvC-pqq Find the full research paper here: https://lnkd.in/gcTsmqzz #MachineLearning #DataManagement #Data #AI #Research #SIGMOD2024

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Scalable Multi-Agent Reinforcement Learning Framework for Efficient Decision-Making in Large-Scale Systems https://lnkd.in/gPqSA-CQ

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Large Generative Graph Models (LGGMs): A New Class of Graph Generative Model Trained on a Large Corpus of Graphs Quick read: https://lnkd.in/gt_GbngB Paper: https://lnkd.in/gP27y6Gq

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Explore fascinating free tutorials on machine learning! In "Neural Networks: Zero to Hero" Andrej Karpathy explains the fundamental themes of ML with the real usage of code. Details 👇 You start learning with a simple bigram character-level language model and gradually build it into a more complex Transformer model like GPT. Key topics include: - torch.Tensor usage for efficient neural network evaluation - language modeling framework - MLP implementation - Batch Normalization for robust training - prompting strategies and fine-tuning - tokenization, development of a Tokenizer for GPT models "Neural Networks: Zero to Hero" tutorials by Andrej Karpath on YouTube: https://lnkd.in/eAquTgX3

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Microsoft researchers developed Orca 2, using a technique that allows small language models to achieve performance nearly on par with larger models by following more cautious reasoning strategies. Read our summary of the paper in #TheBatch: https://hubs.ly/Q02BpKy90

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🐍📰 Primer on Python Decorators In this tutorial, you'll look at what Python decorators are and how you define and use them. Look at how decorators work under the hood and practice writing your decorators. #python

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🕵️ Want a sneak peek at Python's future performance features? This #PyConUS24 talk by Ken Jin & Jules Poon dives into the experimental bytecode optimizer & the journey of 2 undergrads contributing to CPython. 👀 Find out how you can get involved in open source development! 💻 https://buff.ly/43KKptS. #Python #OpenSource

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SynDL: A Synthetic Test Collection Utilizing Large Language Models to Revolutionize Large-Scale Information Retrieval Evaluation and Relevance Assessment Researchers from University College London, University of Sheffield, Amazon, and Microsoft introduced a new test collection named SynDL. SynDL represents a significant advancement in the field of IR by leveraging LLMs to generate a large-scale synthetic dataset. This collection extends the existing TREC Deep Learning Tracks by incorporating over 1,900 test queries and generating 637,063 query-passage pairs for relevance assessment. The development process of SynDL involved aggregating initial queries from the five years of TREC Deep Learning Tracks, including 500 synthetic queries generated by GPT-4 and T5 models. These synthetic queries allow for a more extensive analysis of query-document relationships and provide a robust framework for evaluating the performance of retrieval systems. The core innovation of SynDL lies in its use of LLMs to annotate query-passage pairs with detailed relevance labels. Unlike previous collections, SynDL offers a deep and wide relevance assessment by associating each query with an average of 320 passages. This approach increases the scale of the evaluation and provides a more nuanced understanding of the relevance of each passage to a given query. SynDL effectively bridges the gap between human and machine-generated relevance judgments by leveraging LLMs’ advanced natural language comprehension capabilities. The use of GPT-4 for annotation has been particularly noteworthy, as it enables high granularity in labeling passages as irrelevant, related, highly relevant, or perfectly relevant..... Read our full take on this: https://lnkd.in/gATdzyyQ Paper: https://lnkd.in/gQGnGbGY

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Trail of Bits summer intern Jason An has significantly enhanced Pwndbg, a popular GDB plugin for reverse engineering. An's contributions bring two major improvements to stripped binary analysis: 🥷 Binary Ninja Integration: Pwndbg now seamlessly integrates with Binary Ninja, providing rich debugging information and syncing decompilation, breakpoints, symbols, and function signatures. 🟢 Go Structure Dumping: A new 'go-dump' command allows for improved debugging of Go binaries by enabling the dumping of Go values. These enhancements bring an IDE-like experience to GDB with Pwndbg, particularly beneficial for analyzing stripped binaries. The new features are available in the latest Pwndbg release, marking a significant step forward in reverse engineering capabilities.

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Trail of Bits summer intern Jason An has significantly enhanced Pwndbg, a popular GDB plugin for reverse engineering. An's contributions bring two major improvements to stripped binary analysis: 🥷 Binary Ninja Integration: Pwndbg now seamlessly integrates with Binary Ninja, providing rich debugging information and syncing decompilation, breakpoints, symbols, and function signatures. 🟢 Go Structure Dumping: A new 'go-dump' command allows for improved debugging of Go binaries by enabling the dumping of Go values. These enhancements bring an IDE-like experience to GDB with Pwndbg, particularly beneficial for analyzing stripped binaries. The new features are available in the latest Pwndbg release, marking a significant step forward in reverse engineering capabilities.

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Python Question 69:  Defanging an IP Address and URL Full Video: https://lnkd.in/g2CdVau2 Video 193: In this video, we are going to defang an ip address. We are given a valid ip address or URL and asked to return a defanged version of that value. In a defanging process, we replace every period "." with "[.]" or other representation. In this exercise, we are going to use [.] as the defanging representation In this video, we are going to discuss three different approaches to address this defanging process. *** Approach 1: Using FOR loop ***   This approach uses a for loop to iterate over each character in the input address. If the character is a period ('.'), it replaces it with '[.]'. Otherwise, it keeps the character unchanged. This method is straightforward and easy to understand, but it involves more lines of code compared to other approaches. *** Approach 2: List Comprehension ***   In this approach, a list comprehension is used to iterate over each character in the input address. If the character is a period ('.'), it is replaced with '[.]'. Otherwise, it remains unchanged. The resulting list is then joined into a single string. *** Approach 3: Using REPLACE() function ***   This approach utilizes the replace() function, which replaces all occurrences of a specified substring with another substring. Here, we replace every period ('.') in the input address with '[.]'. For a comprehensive understanding and practical demonstration, be sure to watch the entire video. Happy coding! code: https://lnkd.in/gfbSUQes Follow me on, Website : https://growwithdata.co/ YouTube : https://lnkd.in/gSrqE3Nj TikTok : https://lnkd.in/gqFiMex2 LinkedIn : https://lnkd.in/gdfNNX9a Facebook : https://lnkd.in/gU6JWpFH FB Group : https://lnkd.in/gG4ENcrP twitter : https://lnkd.in/geZ3dqi8 WhatsApp : https://lnkd.in/gBa5eC7y Instagram : https://lnkd.in/gn263_PZ TheWide : https://lnkd.in/gt7htz_8 #recursive #countingalgorithm #list #looping #listmanipulation #uniquesubstring #longestsubstring #slicing #lineartimecomplexity #python #pythonquestions #pythontest #pythonprogramming #pythontutorial #python3 #pythonforbeginners #interviewquestions #interview #dataengineers #deinterview #pythoninterview #interviewquestions #leetcode #placementpreparation #meta #google #facebook #apple #netflix #amazon #google #faang #maanga #dataengineers #alphanumeric #nonalphanumeric #growwithdata #set #split #remove #pythonfunctions #tamil #tamilpython #tamilinterview #tamilinterviewlatest #PythonInterviewQuestions #PythonInterviewQuestionsAndAnswers #ImportantPythonInterviewQuestions #PrepareForPythonJobs #PythonJobInterviewPreparation #TopPythonInterviewQuestions #CommonPythonInterviewQuestions #growwithdata

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