Hybrid Attention for Binary Sequence Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Combining n-Gram Embeddings, Count-Aware Self-Attention, and Recency-Weighted ARMA for Multi-Horizon Distributional PredictionsPhoto by Pasqualino Capobianco on Unsplash I tackle pure binary time series forecasting by converting complex signals into 0/1 patterns — stock up/down, …
Designing Customized and Dynamic Prompts for Large Language Models
Author(s): Shenggang Li Originally published on Towards AI. A Practical Comparison of Context-Building, Templating, and Orchestration Techniques Across Modern LLM FrameworksPhoto by Free Nomad on Unsplash Imagine you’re at a coffee shop, and ask for a coffee. Simple, right? But if you …
A Novel and Practical Meta‑Booster for Supervised Learning
Author(s): Shenggang Li Originally published on Towards AI. A Stacking‑Enhanced Margin‑Space Framework for Dynamic, Loss‑Driven Ensemble Updates in Classification and RegressionPhoto by Thorium on Unsplash Ensemble methods thrive on diversity, yet most frameworks exploit it sequentially (boosting) or statically (stacking). We introduce …
Adaptive Decay-Weighted ARMA: A Novel Approach to Time Series Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Integrating Recency-Based Loss Weighting and Seasonal Feature Tuning for Enhanced Predictive AccuracyPhoto by Haberdoedas II on Unsplash Time series forecasting is both fascinating and challenging. It’s fascinating because accurate predictions can directly inform better …
Beyond Simple Inversion: Building and Applying Inverse Neural Networks
Author(s): Shenggang Li Originally published on Towards AI. Theory, training tricks, and real‑world case studies — solving multi‑root equations and beyondPhoto by Marisa Harris on Unsplash Inverse problems ask a fundamental question: Given the output y, what was the input x? Traditional …
Reinforcement Learning-Enhanced Gradient Boosting Machines
Author(s): Shenggang Li Originally published on Towards AI. A Novel Approach to Integrating Reinforcement Learning within Gradient Boosting Internal Optimization for Superior Predictive PerformancePhoto by Austin Neill on Unsplash In this post, I demonstrate how reinforcement learning (RL) can directly enhance the …
Adaptive Multi-Teacher Distillation for Enhanced Supervised Learning
Author(s): Shenggang Li Originally published on Towards AI. A Novel Approach for Dynamically Combining Multiple Predictive Models into a Lightweight High-Performance Student ModelPhoto by Damon Hall on Unsplash In practical supervised learning, using a single predictive model like XGBoost, LightGBM, or Random …
Reimagining Diffusion Models: Autoregressive Priors for Efficient Initialization
Author(s): Shenggang Li Originally published on Towards AI. Exploring a Novel Approach to Diffusion Initialization with Intuitive Illustrations, Applications This member-only story is on us. Upgrade to access all of Medium. Photo by Gary Fultz on Unsplash Diffusion models have become a …
Practical Guide to Distilling Large Models into Small Models: A Novel Approach with Extended Distillation
Author(s): Shenggang Li Originally published on Towards AI. Comparing Traditional and Enhanced Step-by-Step Distillation: Adaptive Learning, Cosine Similarity, and Curriculum-Based Rationale Supervision This member-only story is on us. Upgrade to access all of Medium. Photo by Thorium on Unsplash In this paper, …
Exploring LoRA as a Dynamic Neural Network Layer for Efficient LLM Adaptation
Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Photo by Jakub Żerdzicki on Unsplash LLMs need constant updates — legal AI must learn new laws, finance chatbots need fresh market …
Building AI-Powered Chatbots with Gemini, LangChain, and RAG on Google Vertex AI
Author(s): Shenggang Li Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. A Step-by-Step Guide to Configuring Google Vertex AI, Leveraging the Gemini API, and Integrating Knowledge Bases for Intelligent Conversational Applications Photo by …
Investigating Transformer Attention and Reinforcement Learning Dynamics Using Self‑Generated Structural Data
Author(s): Shenggang Li Originally published on Towards AI. Cracking the Code: Synthetic Data as the Key to Understanding and Enhancing LLMs This member-only story is on us. Upgrade to access all of Medium. Photo by Joshua Sortino on Unsplash Building large language …
A Neural Sparse Graphical Model for Variable Selection and Time-Series Network Analysis
Author(s): Shenggang Li Originally published on Towards AI. A Unified Adjacency Learning and Nonlinear Forecasting Framework for High-Dimensional Data This member-only story is on us. Upgrade to access all of Medium. Photo by Susan Q Yin on Unsplash Imagine a spreadsheet with …
Beyond Buy-and-Hold: Dynamic Strategies for Unlocking Long-Term Stock Growth
Author(s): Shenggang Li Originally published on Towards AI. Harnessing Survival Analysis and Markov Decision Processes to Surpass Static ETF Performance This member-only story is on us. Upgrade to access all of Medium. Photo by Chris Liverani on Unsplash Have you ever heard …
DeepSeek-TS+: A Unified Framework for Multi-Product Time Series Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Leveraging State-Space Enhanced Multi-Head Latent Attention and Group Relative Policy Optimization (GRPO) for Adaptive Forecasting This member-only story is on us. Upgrade to access all of Medium. Photo by Solen Feyissa on Unsplash I …
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