Artificial Intelligence and Robotics
Explore AI and robotics concepts including search algorithms, expert systems, natural language processing, neural networks, and robotic applications through Hindi-language explanations and examples.
Evaluate LayoutLMv3 for Document Classification - Save & Load Model to HuggingFace Hub
Learn to evaluate LayoutLMv3 for document classification, save and load models to HuggingFace Hub, and analyze performance using confusion matrices in this hands-on tutorial.
Building an Advanced Agentic RAG System with Langchain, Ollama, and Milvus
Explore the cutting-edge world of Agentic Retrieval-Augmented Generation (RAG) systems in this 31-minute conference talk from Devoxx. Discover how to construct an advanced Agentic RAG system using open-source technologies like Langchain, Ollama, and Milv…
Configurable Fairness for New Item Recommendation Considering Entry Time - SIGIR 2024
Explore configurable fairness in recommender systems, focusing on new item recommendations and entry time considerations for enhanced algorithmic equity.
Fair Recommendations with Limited Sensitive Attributes - A Distributionally Robust Optimization Approach
Explore distributionally robust optimization for fair recommendations with limited sensitive attributes in RecSys, enhancing fairness in machine learning applications.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations - Lecture 7
Explore adaptive fair representation learning for personalized fairness in recommendations through information alignment techniques.
Going Beyond Popularity and Positivity Bias: Correcting Multifactorial Bias in Recommender Systems - M1.7
Explore techniques for addressing multiple biases in recommender systems, focusing on fairness and accuracy beyond popularity and positivity.
CaDRec: Contextualized and Debiased Recommender Model - Fairness in RecSys
Explore a contextualized and debiased recommender model for fair and accurate recommendations in information retrieval systems.
Reinforcement Learning-based Recommender Systems with Large Language Models - SIGIR 2024
Explore reinforcement learning-based recommender systems using LLMs for state reward and action modeling in this cutting-edge research presentation.
Fair Sequential Recommendation without User Demographics - SIGIR 2024
Explore fair sequential recommendation techniques without relying on user demographics, enhancing ethical AI practices in recommender systems.
Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6
Explore data-efficient fine-tuning techniques for LLM-based recommendation systems, enhancing performance with minimal data requirements.
Uncontextualized Significance Considered Dangerous in Evaluation - Session M1.5
Explore the dangers of uncontextualized significance in evaluation, presented by Nicola Ferro and Mark Sanderson at SIGIR 2024.
The Treatment of Ties in Rank-Biased Overlap - Evaluation M1.5
Explore the treatment of ties in Rank-Biased Overlap, examining its impact on information retrieval evaluation metrics and ranking systems.
Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval - Lecture 3
Explore innovative techniques for enhancing first-stage retrieval efficiency through document expansion and filtering, improving search engine performance.
Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations
Explore efficient inverted indexes for approximate retrieval using learned sparse representations, enhancing search performance and accuracy.
Ranked List Truncation for Large Language Model-based Re-Ranking - Efficiency for Search
Explore efficient ranked list truncation techniques for LLM-based re-ranking in search systems, enhancing performance and reducing computational costs.
Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion
Explore innovative techniques for enhancing multimodal knowledge graph completion through semantic neighbor retrieval and contrastive learning.
EditKG: Editing Knowledge Graph for Recommendation - Lecture 1
Explore innovative techniques for editing knowledge graphs to enhance recommendation systems, focusing on improving accuracy and relevance.
Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graphs
Explore transformer-based reasoning for learning evolutionary event chains on temporal knowledge graphs, enhancing understanding of complex temporal relationships.
Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models - Session M1.1
Explore a setwise approach for zero-shot ranking using Large Language Models, enhancing effectiveness and efficiency in information retrieval tasks.
Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images - Multimedia 1
Explore text-image retrieval models' bias towards AI-generated images and its implications for multimedia search and content creation.