Lectures

Lecture 1/3 “Just a stochastic parrot? How LLMs Learn, Reason, and Self-Improve”

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Lecture 2/3 “A Skill-based view of LLM capabilities and their emergence”

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Lecture 3/3 ”LLM Metacognition: Eliciting and Leveraging LLMs’ “Thinking about Thinking” ”

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Lecture 1/3 “Diffusion models: Intuition and Perspectives”

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Lecture 2/3 “Diffusion models: Guidance, Distillation and Advanced Topics”

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Lecture 3/3 “How to Train Neural Nets Effectively”

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Lecture 1/3 “Fine-Tuning Language Models”

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Tentatively, my lectures will be on the post-training of large language models.

Lecture 2/3 “Reinforcement Learning for Language Models”

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Lecture 3/3 “Applications: Alignment for Safety and Reasoning for Scientific Discovery”

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Lecture 1/3 “Transformer Architectures”

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Lecture 2/3 “Language Models Pre-Training and Scaling – I”

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Lecture 3/3 “Language Models Pre-Training and Scaling – II”

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Lecture 1/3

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Lecture 2/3

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Lecture 3/3

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Lecture 1/3 “Reasoning and Planning Abilities of the Large Language Models”

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Lecture 2/3 “Reasoning and Planning Abilities of the Large Language Models”

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Lecture 3/3 “Reasoning and Planning Abilities of the Large Language Models”

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Lecture 1/3 “Multi-Agent Deep Reinforcement Learning”

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Lecture 2/3 “Cooperative AI”

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Lecture 3/3 “Generative Agents”

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Lecture 1/4

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Lecture 2/4

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Lecture 3/4

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Lecture 4/4

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Lecture 1/3 “Learning on Graphs: The Essentials”

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Lecture 2/3 “Challenges of using Graph Neural Networks”

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Lecture 3/3 “Graph Reasoning with Large Language Models”

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Lecture

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Lecture “On some Challenges of Embeddings Theory”

Abstract: Embeddings are a ubiquitous topic, which is now being used in various areas of the artificial intelligence studies, from knowledge graphs to LLMs. Specifically, an active area of research in computer science is the theory of manifold learning and finding lower-dimensional manifold representation on how we can learn geometry from data for providing better quality curated datasets. Yet for this usually we need to accept the set assumptions on the geometry of the feature space.
In this talk we will specifically be interested in speaking about the main challenges of the embedding theory, as well as talking about some foundations which help to foster its explainability and interpretability aspects. For this we will cover several interrelated aspects of finite metric spaces theory, applications of embedding theory to knowledge graphs, as well as learning data geometry and data curation for large language models. This work is based on several works:
Singh, LT et al. PlosOne (2023),
Singh, LT et al. EPJ D.S. 13,12 (2024)
LT, Kathuria, Compl.Net. Proc. (2024)