Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences
cs.AI, cs.LG
Submitted: 2026-03-17
Updated: 2026-08-27
Code: https://github.com/goodfeli/dlbook_notation
Terminology
Sources
- Your Autoregressive Model Already Reveals the Causal Graph
- Deep Learning using Rectified Linear Units (ReLU)
- Chronos: Learning the Language of Time Series
- Small Language Models are the Future of Agentic AI
- Longformer: The Long-Document Transformer
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Multi-Agent Causal Discovery Using Large Language Models
- An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
- A tutorial introduction to the minimum description length principle
- Deep Autoregressive Models as Causal Inference Engines
- A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
- Causal Discovery in Hawkes Processes by Minimum Description Length
- Feature Selection: A Data Perspective
- Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks
- GPT-4 Technical Report
- AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
- Self-Supervised Contrastive Pre-Training for Multivariate Point Processes
- Bayesian Network Structural Consensus via Greedy Min-Cut Analysis
- LLaMA: Open and Efficient Foundation Language Models
- Linformer: Self-Attention with Linear Complexity
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