Categories

Adversarial learning studies model behavior under intentionally crafted input perturbations and the threat models that produce them. Practical work focuses on generating attacks (e.g., FGSM, PGD), hardening via adversarial training or certified defenses, and evaluating clean vs. robust accuracy across attack strengths.

AI for time-series forecasting combines classical statistical models with sequence models to predict future values and quantify uncertainty. Practical approaches include ARIMA baselines, RNN/Transformer architectures, walk-forward validation, and probabilistic losses (quantile, CRPS) for uncertainty-aware forecasts.

AI accelerates virtual screening, molecular property prediction, and generative design using graph and sequence-based models. Techniques include GNNs for property prediction, generative VAEs/flows for molecule design, and domain-aware evaluation with metrics like QED and docking scores.

AI on embedded systems prioritizes low latency and energy by using quantization, pruning, and lightweight architectures. Workflows involve quantization-aware training or post-training quantization, hardware-aware optimizations (TVM, TF Lite), and benchmarking on target devices.

AI in scientific computing uses neural networks to accelerate PDE solvers, build surrogates, and solve inverse problems while respecting physical constraints. Methods include PINNs and operator learning (DeepONet, FNO), with careful loss weighting, numerical stability checks, and error norms compared to traditional solvers.

Model compression reduces size and compute via pruning, quantization, distillation, and low-rank factorization. Typical workflow profiles bottlenecks, applies structured compression for hardware speedups, and fine-tunes to recover accuracy.

Notes and guides about the Astro framework, content tooling, and site architecture used on this website. Covers component patterns, static rendering, content collections, and deployment considerations for Astro-based sites.

AutoML automates model selection, pipeline construction, and hyperparameter tuning using search strategies like Bayesian optimization and evolutionary search. Practical systems combine multi-fidelity evaluations, meta-learning for warm-starts, and hardware-aware objectives to manage compute budgets.

Bayesian deep learning integrates Bayesian inference with neural networks to provide calibrated uncertainty estimates. Approaches include variational BNNs, Monte Carlo dropout, and deep ensembles, with evaluation via calibration metrics and predictive intervals.

Bayesian methods model uncertainty by treating parameters and predictions as probability distributions. Common practices include MCMC and variational inference for posterior estimation, Gaussian processes for nonparametric modeling, and careful prior selection and diagnostics for robust inference.

Causal inference focuses on identifying cause-effect relationships using causal graphs, potential outcomes, and do-calculus. Techniques include propensity scores, instrumental variables, doubly-robust learners, and careful sensitivity analysis to unobserved confounding.

Contrastive learning trains representations by bringing similar examples closer and pushing dissimilar ones apart, often using strong augmentations and a contrastive objective. Popular methods include SimCLR and MoCo, with evaluation via linear probes and transfer learning performance.

Cross-lingual NLP develops models that transfer understanding across languages using multilingual pretraining, alignment, and shared subword tokenization. Techniques include multilingual transformers, zero-shot transfer, and adaptation via parallel corpora or adapters.

Data augmentation increases data diversity via domain-specific transforms, synthetic examples, and augmentation policies. Strong augmentation pipelines (for images, text, time series) and automated methods (AutoAugment, RandAugment) improve robustness and generalization when applied carefully.

Deep RL combines function approximation with sequential decision-making to learn policies from interaction. Core algorithms include policy gradients (PPO, A3C), value-based methods (DQN), and actor-critic hybrids, with practical concerns around stability, reward shaping, and sample efficiency.

Dimensionality reduction projects high-dimensional data into lower-dimensional spaces for visualization, compression, or denoising. Methods include linear techniques (PCA, LDA), manifold methods (t-SNE, UMAP), and learned approaches using autoencoders.

Dynamic graph networks model temporal changes in graph structure or node features using sequence-aware GNNs or temporal point-process models. Approaches include temporal message passing, recurrent graph updates, and continuous-time dynamic graph models for streaming data.

Edge AI runs inference on-device to reduce latency and preserve privacy, emphasizing optimized models and efficient runtimes. Techniques include model compression, on-device accelerators, and careful resource management for limited-memory and low-power environments.

Evolutionary algorithms use populations, mutation, crossover, and selection to search complex optimization spaces. They are commonly applied to hyperparameter search, neuroevolution, and multi-objective optimization where gradients are unavailable or unreliable.

XAI focuses on model transparency, interpretability, and actionable explanations for predictions. Techniques include feature attribution (SHAP, LIME), surrogate models, counterfactual explanations, and global interpretability methods tailored to stakeholders.

Feature engineering extracts informative features from raw data using domain knowledge, transformations, and aggregation. Common practices include handling missingness, encoding categorical variables, creating lag/rolling features for time series, and careful normalization/scaling.

Federated learning trains models across decentralized devices while keeping data local to preserve privacy. Challenges include communication efficiency, heterogeneity of client data, secure aggregation, and robust aggregation strategies.

Few-shot learning aims to generalize from few labeled examples using meta-learning, prototypical networks, or transfer learning. Methods include episodic training, metric learning, and parameter-efficient fine-tuning to improve sample efficiency.

GANs train a generator against a discriminator in an adversarial loop to learn realistic data distributions. Variants (WGAN, StyleGAN, conditional GANs) focus on stability, mode coverage, and conditional generation for images, audio, and other modalities.

Graph-based learning applies machine learning to relational data using graph representations and specialized models. Common tasks include node classification, link prediction, and graph-level prediction using GNNs and spectral methods.

Graph neural networks generalize deep learning to graph-structured data using message-passing and aggregation. Architectures (GCN, GAT, MPNN) model node and edge features for tasks like node classification, link prediction, and graph-level regression.

Personal notes, projects, and research summaries related to Gregory Mikuro's work. Includes project overviews, publications, and technical writeups across machine learning and applied research.

Hyperparameter tuning searches for optimal model and training hyperparameters using techniques like grid search, random search, and Bayesian optimization. Multi-fidelity methods (Hyperband) and parallel schedulers speed up searches while managing compute budgets.

LLMs are transformer-based architectures pretrained on large corpora to learn broad linguistic patterns and world knowledge. Practical usage includes fine-tuning, prompt engineering, instruction tuning, and evaluation for alignment and hallucination mitigation.

Meta-learning trains models to learn new tasks quickly by learning across tasks, often via gradient-based or metric-based approaches. Popular methods include MAML, prototypical networks, and optimization-based meta-learners for few-shot adaptation.

Model deployment covers packaging trained models into reliable, scalable services with CI/CD, monitoring, and versioning. Focus areas include model serving frameworks, input/output validation, latency/throughput tuning, and logging for drift detection.

Model interpretability provides insights into model decisions using local and global explanation techniques. Methods include feature attributions, surrogate models, partial dependence plots, and concept-based interpretability.

Multi-agent systems study interactions among multiple learning agents with cooperative or competitive objectives. Topics include decentralized training, communication protocols, game-theoretic equilibria, and credit assignment in cooperative tasks.

Multimodal learning fuses information from different modalities (text, image, audio, 3D) using cross-modal encoders and attention. Architectures combine modality-specific encoders with fusion modules and joint objectives for alignment and retrieval.

MySQL notes for data storage and query patterns used in data pipelines and experiment tracking. Includes schema design, indexing tips, and considerations for ETL with ML datasets.

NAS automates the discovery of neural architectures using gradient-based search, weight-sharing, or evolutionary methods. Practical NAS emphasizes efficiency via weight sharing, multi-fidelity evaluation, and hardware-aware objectives.

Covers canonical architectures (CNNs, RNNs, Transformers) and design patterns such as residual connections, attention mechanisms, and normalization layers. Focuses on architecture choices that match data modality and compute constraints.

Neuro-symbolic AI combines neural methods with symbolic reasoning to capture structure, logic, and generalization. Approaches integrate differentiable reasoning modules, symbolic constraints, or program induction to improve interpretability and compositionality.

Optimization algorithms (SGD, Adam, RMSProp, L-BFGS) drive model training with trade-offs in convergence and generalization. Proper learning rate schedules, momentum, and adaptive methods are crucial for training deep networks robustly.

Optimization in RL focuses on stable gradient estimators, variance reduction, and constrained optimization for policy learning. Methods include trust-region techniques (TRPO), PPO, and natural gradient approaches to improve convergence and policy stability.

Notes on PHP for web development, including integrations with static site generators and server-side rendering patterns. Focuses on practical tips for backend routing, templating, and deployment where relevant.

Python notes on tooling, idioms, and libraries commonly used in machine learning projects, including packaging and virtual environments. Topics include efficient data handling with NumPy/Pandas, training loops with PyTorch/TensorFlow, and reproducibility practices.

Quantum machine learning explores hybrid quantum-classical algorithms and parameterized quantum circuits for tasks like classification and optimization. Work is experimental and focuses on variational circuits, encoding classical data into quantum states, and analyzing potential quantum advantage.

Regularization prevents overfitting using methods like weight decay, dropout, data augmentation, and early stopping. Advanced techniques include mixup, label smoothing, and Bayesian priors for principled regularization.

Reinforcement learning learns policies through interaction to maximize cumulative reward using algorithms like DQN, PPO, and SAC. Key engineering issues include sample efficiency, exploration strategies, and stable value/policy updates.

Scalability in AI addresses training and serving models at large scale, covering distributed training, data-parallel and model-parallel strategies. Practical concerns include communication overheads, sharding, mixed precision, checkpointing, and autoscaling for production serving.

Self-learning AI refers to systems that iteratively improve using their own predictions or weak supervision signals. Approaches include self-training, pseudo-labeling, and continual learning with mechanisms to prevent drift and catastrophic forgetting.

Self-supervised learning creates pretext tasks to learn representations without labels, often via contrastive or generative objectives. These representations enable strong transfer performance on downstream tasks with limited labeled data.

Sequence modeling addresses ordered data such as text, audio, and time series using RNNs, TCNs, or transformers. Key considerations include handling long-range dependencies, choice of positional encodings, and decoding strategies for generation tasks.

Supervised learning trains models on labeled data to predict targets, using losses like cross-entropy or MSE and evaluation metrics tailored to tasks. Common algorithms range from linear models and tree ensembles to deep neural networks with appropriate regularization and validation strategies.

Tokenization converts raw text into model-friendly subword or token sequences, balancing vocabulary size and coverage. Choices (byte-pair encoding, unigram, WordPiece) impact handling of rare words, multilingual text, and downstream model efficiency.

Tools covers practical software, libraries, and utilities used in machine learning workflows. Includes build tools, runtimes, evaluation libraries, and deployment pipelines for reproducible experiments.

Transfer learning reuses pretrained models or features to speed up learning on new tasks with limited data. Techniques include fine-tuning, feature extraction, and adapters to trade off performance and compute cost.

Unsupervised learning discovers structure from unlabeled data using clustering, density estimation, and representation learning. Methods include k-means, Gaussian mixtures, autoencoders, and contrastive/self-supervised approaches for embedding learning.

'What Is' explains foundational concepts and definitions in machine learning and AI in concise, accessible terms. Each entry focuses on core ideas, typical use cases, and brief pointers to deeper technical resources.

Zero-shot learning enables models to generalize to unseen classes using semantic attributes, prompts, or shared embeddings. Common approaches include embedding alignment, attribute-based classifiers, and large pretrained models with appropriate prompts.

Comprehensive coverage of computer science research beyond AI/ML, including algorithms, data structures, computer systems, networking, security, human-computer interaction, and theoretical foundations of computing.

Analysis of research in economics, sociology, psychology, and related social sciences, with particular attention to quantitative methods, behavioral studies, and interdisciplinary approaches to understanding social phenomena.

Reviews of research in various engineering disciplines including mechanical, electrical, civil, and biomedical engineering, with emphasis on innovative design approaches, computational modeling, and practical applications.

Critical examination of research in climate science, ecology, conservation, renewable energy, and sustainability, with focus on methodological approaches, data analysis techniques, and cross-disciplinary implications.

Explorations in mathematical theory, computational methods, algorithms, and applications across pure and applied mathematics, including number theory, optimization, algebra, and computational geometry.

Analysis of research in clinical medicine, medical technologies, healthcare systems, and therapeutic approaches, with a focus on interdisciplinary connections to other fields and evidence-based methodologies.

Reviews and analysis of research in theoretical and experimental physics, including quantum mechanics, astrophysics, condensed matter physics, and computational models that advance our understanding of fundamental physical principles.