Do generative video models learn physical principles from watching videos? This paper explores whether generative video models learn physical principles by analyzing their performance on the Physics-IQ benchmark, which tests understanding of laws like fluid dynamics, optics, and
MACHINE LEARNING
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Diffusion Model for Portrait Animation: Joint Depth and Appearance Learning
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Joint Learning of Depth and Appearance for Portrait Image Animation This research presents a diffusion-based generative model for joint learning of depth and appearance in portrait image animation, enabling consistent visual and 3D outputs for various applications. Problem:
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WebWalker: LLM Benchmarking Framework for Web Traversal
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WebWalker: Benchmarking LLMs in Web Traversal The paper introduces WebWalkerQA, a benchmark for testing LLMs in web traversal, and WebWalker, a multi-agent framework for human-like navigation to extract layered information. Problem: RAG systems retrieve shallow content,
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GPT-4o Advances Face Presentation Attack Detection Few-Shot Learning
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Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning The paper investigates GPT-4o for Face Presentation Attack Detection (PAD), showing strong performance in few-shot in-context learning, outperforming specialized PAD models in
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MIT Introduces Boltz-1 Open-Source Biomolecular Structure Prediction
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MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures https://
bit.ly/3P4sTtZ
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
7 In-Demand Skills to Land a Data Analytics Job
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Want to land a job in Data Analytics? These 7 in-demand skills will get you hired: Technical Skills: SQL, Spreadsheets
Programming: R, Python, SAS
Data Viz: Tableau, Power BI
Database Management: MySQL, PostgreSQL
Machine Learning: Regression, SVM
Soft Skills: Critical -
Learning RAG, LLMs, AI Agents, and ML/AI Engineering
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Want to learn more about: – RAG – LLMs – AI Agents – ML/AI Engineering Find me →
@akshay_pachaar -
ReAct: Combining Reflection and Tool Use for Agents
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ReAct (Reasoning and Acting) ReAct combines Reflection and Tool use patterns. Now the agent can reflect on both the LLM-generated outputs and the results of its interactions with the external world using tools. One of the most popular and powerful patterns used today.
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Reflection Pattern: AI Self-Critique and Iterative Refinement Cycle
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Reflection Pattern In this pattern, the AI “reviews” its own work—spotting mistakes and iterating until the final outcome is polished. Think of it like a self-critique cycle where the model refines each draft before presenting the result!
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Top 5 Agentic AI Design Patterns Clearly Explained
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Top 5 Agentic AI design patterns, clearly explained (with visuals):