- Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding [Paper]
- Rethinking Tabular Data Understanding with Large Language Models [Paper]
- Corrective Retrieval Augmented Generation [Paper]
- Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs [Paper]
- Self-Rewarding Language Models [Paper]
- War and Peace (WarAgent): LLM-based Multi-Agent Simulation of World Wars [Paper]
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [Paper]
- Mathematical Discoveries from Program Search with LLMs [Paper]
- Evaluating Large Language Models: A Comprehensive Survey [Paper]
- Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks [Paper]
- HyperFast: Instant Classification for Tabular Data [Paper]
- ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent [Paper]
- Exploiting Novel GPT-4 APIs [Paper]
- Do Androids Know They're Only Dreaming of Electric Sheep? [Paper]
- Retrieval-Augmented Generation for Large Language Models: A Survey [Paper]
- Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? [Paper]
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model [Paper]
- Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM [Paper]
- Large Language Models on Graphs: A Comprehensive Survey [Paper]
- Relational Deep Learning: Graph Representation Learning on Relational Databases [Paper]
- PDFTriage: Question Answering over Long, Structured Documents [Paper]
- From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting [Paper]
- LLMs Are Zero-Shot Time Series Forecasters [Paper]
- Rethinking Tabular Data Understanding with Large Language Models [Paper]
- Table-GPT: Table-tuned GPT for Diverse Table Tasks [Paper]
- MRKL Systems: Modular Reasoning, Knowledge and Language [Paper]
- Mixtral of Experts [Paper]
- Can Generalist Foundation Models Outcompete Special-Purpose Tuning? [Paper]
- Let's Verify Step By Step [Paper]
- Navigating the Jagged Technological Frontier [Paper]
- AI Canon [Blogpost]
- Mixture of Experts Explained [Blogpost]
- Challenges and Applications of Large Language Models [Paper]
- Open Problems and Fundamental Limitations of RLHF [Paper]
- Why AI Will Save The World [Blogpost]
- Google "We Have No Moat, And Neither Does OpenAI" [Blogpost]
- Attention Is All Your Need [Paper]
- Sparks of Artificial General Intelligence: Early Experiments with GPT-4 [Paper]
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) [Paper]
- The Annotated Transformer [Blogpost]
- The Illustrated Transformer [Blogpost]
- The Illustrated GPT-2: Visualize Transformer Language Models [Blogpost]
- How GPT3 Works: Visualizations and Animations [Blogpost]
- Five Years of GPT Progress [Blogpost]
- Understanding Large Language Models [Blogpost]
- RLHF: Reinforcement Learning from Human Feedback [Blogpost]
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines [Paper]
- RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval [Paper]
- Building LLM Applications for Production [Blogpost]
- Best Practices for LLM Evaluation of RAG Applications [Blogpost]
- Emerging Architectures for LLM Applications [Blogpost]
- Patterns for Building LLM-based Systems & Products [Blogpost]
- RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture [Paper]
- OpenAI Prompt Engineering Guide [Blogpost]
- Graph of Thoughts: Solving Elaborate Problems with LLMs [Paper]
- Chain-of-Verification Reduces Hallucination in LLMs [Paper]
- ReAct: Synergizing Reasoning and Acting in Language Models [Paper]
- Chain-of-Thought Prompting Elicits Reasoning in LLMs [Paper]
- Tree of Thoughts: Deliberate Problem Solving with LLMs [Paper]
- LLM-Rec: Personalized Recommendation via Prompting LLMs [Ppaer]
- ML Engineering for Production Specialization [Course]
- DeepLearning.AI LLM Short Courses [Course]
- LLMs: Application Through Production [Course]
- LLMs: Foundation Models from the Ground Up [Courses]
- Generative AI with Large Language Models [Course]
- Building LLM-Powered Apps [Course]
- The Full Stack LLM Bootcamp [Course]
- Neural Networks From Zero to Hero [Course]
- Lakehouse: A New Generation of Open Platforms [Paper]
- Machine Learning and Causality: The Impact of Financial Crises on Growth [Paper]
- A Comparative Study of Hyper-Parameter Optimization Tools [Paper]
- WeightedSHAP: analyzing and improving Shapley based feature attributions [Paper]
- Machine Learning Operations (MLOps): Overview, Definition, and Architecture [Paper]
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [Paper]
- Deep Residual Learning for Image Recognition [Paper]
- A Unified Approach to Interpreting Model Predictions [Paper]
- Focal Loss for Dense Object Detection [Paper]
- Cyclical Learning Rates for Training Neural Networks [Paper]
- Entity Embeddings of Categorical Variables [Paper]
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in ML [Paper]
- An Overview of Gradient Descent Optimization Algorithms [Blogpost]
- An Updated Overview of Gradient Descent Optimization Algorithms [Blogpost]
- A Recipe for Training Neural Networks[Blogpost]
- Deep Neural Nets: 33 Years Ago and 33 Years From Now [Blogpost]
- DAC: Deep Autoencoder-Based Clustering [Paper]
- XGBSE: Improving XGBoost for Survival Analysis [Blogpost]
- Survival Regression with Accelerated Failure Time Model in XGBoost [Paper]
- Random Survival Forests [Paper]
- Understanding Survival Analysis: Kaplan-Meier Estiamte [Paper]
- Predicting Customer Lifetime Values: E-Commerce Use Case [Paper]
- A Deep Probabilistic Model for Customer Lifetime Value Prediction [Paper]
- Churn Prediction with Sequential Data and Deep Neural Networks [Paper]
- Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data [Paper]
- Behavioral Modeling for Churn Prediction [Paper]
- Deep & Cross Network for Ad Click Predictions [Paper]
- Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning [Paper]
- Temporal Graph Networks for Deep Learning on Dynamic Graphs [Paper]
- A Review on Graph Neural Network Methods in Financial Applications [Paper]
- A Survey on Graph Representation Learning Methods[Paper]
- On Embeddings for Numerical Features in Tabular Deep Learning [Paper]
- Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data [Paper]
- An Embedding Learning Framework for Numerical Features in CTR Prediction [Paper]
- DCN V2: Improved Deep & Cross Network and Practical Lessons [Paper]
- Revisiting Deep Learning Models for Tabular Data [Paper]
- Tabular Data: Deep Learning is Not All You Need [Paper]
- Deep Neural Networks and Tabular Data: A Survey [Paper]
- XGBoost: A Scalable Tree Boosting System [Paper]
- Transformers in Time Series: A Survey [Paper]
- Forecasting with Trees [Paper]
- Deep Learning for Time Series Forecasting: Tutorial and Literature Survey [Paper]
- DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks [Paper]
- NeuralProphet: Explainable Forecasting at Scale [Paper]
- Prophet: Forecasting at Scale [Paper]
- AR-Net: A simple Auto-Regressive Neural Network for time-series [Paper]
- Conditional Time Series Forecasting with Convolutional Neural Networks [Paper]
- WaveNet: A Generative Model for Raw Audio [Paper]
- An Experimental Review on Deep Learning Architectures for Time Series Forecasting [Paper]
- Do We Really Need Deep Learning Models for Time Series Forecasting? [Paper]
- Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters [Paper]