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Tag: model

  • Stop Asking if a Model Is Interpretable

    Stop Asking if a Model Is Interpretable Start asking what question the explanation should answer. The post Stop Asking if a Model Is Interpretable appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source

  • When LLMs get significantly worse: A statistical approach to detect model degradations

    When LLMs get significantly worse: A statistical approach to detect model degradations arXiv:2602.10144v1 Announce Type: new Abstract: Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and others without accuracy guarantees like quantization. In all of these cases it is crucial to…

  • How to Model The Expected Value of Marketing Campaigns

    How to Model The Expected Value of Marketing Campaigns The approach that takes companies to the next level of data maturity The post How to Model The Expected Value of Marketing Campaigns appeared first on Towards Data Science. Rodrigo Almeida Go to original source

  • MLCBART: Multilabel Classification with Bayesian Additive Regression Trees

    MLCBART: Multilabel Classification with Bayesian Additive Regression Trees arXiv:2601.08964v1 Announce Type: cross Abstract: Multilabel Classification (MLC) deals with the simultaneous classification of multiple binary labels. The task is challenging because, not only may there be arbitrarily different and complex relationships between predictor variables and each label, but associations among labels may exist even after accounting…

  • Online Learning with Limited Information in the Sliding Window Model

    Online Learning with Limited Information in the Sliding Window Model arXiv:2601.03533v1 Announce Type: new Abstract: Motivated by recent work on the experts problem in the streaming model, we consider the experts problem in the sliding window model. The sliding window model is a well-studied model that captures applications such as traffic monitoring, epidemic tracking, and…

  • Model inference for ranking from pairwise comparisons

    Model inference for ranking from pairwise comparisons arXiv:2512.15269v1 Announce Type: cross Abstract: We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this problem and assume that each object has an unobserved strength and that the outcome of…

  • Improving the Accuracy of Amortized Model Comparison with Self-Consistency

    Improving the Accuracy of Amortized Model Comparison with Self-Consistency arXiv:2512.14308v1 Announce Type: new Abstract: Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However, ABI methods are highly sensitive to model misspecification: when observed data fall outside the training distribution (generative scope…

  • Data-Driven Model Reduction using WeldNet: Windowed Encoders for Learning Dynamics

    Data-Driven Model Reduction using WeldNet: Windowed Encoders for Learning Dynamics arXiv:2512.11090v1 Announce Type: new Abstract: Many problems in science and engineering involve time-dependent, high dimensional datasets arising from complex physical processes, which are costly to simulate. In this work, we propose WeldNet: Windowed Encoders for Learning Dynamics, a data-driven nonlinear model reduction framework to build…

  • Optimizing PyTorch Model Inference on AWS Graviton

    Optimizing PyTorch Model Inference on AWS Graviton Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science. Chaim Rand Go to original source

  • Optimizing PyTorch Model Inference on CPU

    Optimizing PyTorch Model Inference on CPU Flyin’ Like a Lion on Intel Xeon The post Optimizing PyTorch Model Inference on CPU appeared first on Towards Data Science. Chaim Rand Go to original source

  • Your Next ‘Large’ Language Model Might Not Be Large After All

    Your Next ‘Large’ Language Model Might Not Be Large After All A 27M-parameter model just outperformed giants like DeepSeek R1, o3-mini, and Claude 3.7 on reasoning tasks The post Your Next ‘Large’ Language Model Might Not Be Large After All appeared first on Towards Data Science. Moulik Gupta Go to original source

  • Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data

    Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data arXiv:2511.10919v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning presents unique challenges due to the lack of explicitly labeled negative samples, particularly in high-stakes domains such as fraud detection and medical diagnosis. To address data scarcity and privacy constraints, we propose a novel transfer learning with…

  • Interpretable Model-Aware Counterfactual Explanations for Random Forest

    Interpretable Model-Aware Counterfactual Explanations for Random Forest arXiv:2510.27397v1 Announce Type: new Abstract: Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to provide explanations. While model-agnostic frameworks such as Shapley values have proved to be convenient and popular, they rarely…

  • Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space

    Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space arXiv:2510.12916v1 Announce Type: new Abstract: Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high dimensional settings. Auxiliary information, such as noisy observations, is typically only available at discrete times, and incorporating it via…

  • A Honest Cross-Validation Estimator for Prediction Performance

    A Honest Cross-Validation Estimator for Prediction Performance arXiv:2510.07649v1 Announce Type: new Abstract: Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the…

  • System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework

    System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework arXiv:2509.16663v1 Announce Type: new Abstract: ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically dependent. In reality,…

  • Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals

    Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals arXiv:2509.15989v1 Announce Type: new Abstract: We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from…

  • Towards a Physics Foundation Model

    Towards a Physics Foundation Model arXiv:2509.13805v1 Announce Type: cross Abstract: Foundation models have revolutionized natural language processing through a “train once, deploy anywhere” paradigm, where a single pre-trained model adapts to countless downstream tasks without retraining. Access to a Physics Foundation Model (PFM) would be transformative — democratizing access to high-fidelity simulations, accelerating scientific discovery,…

  • NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice

    NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice arXiv:2509.07123v1 Announce Type: new Abstract: Nested logit (NL) has been commonly used for discrete choice analysis, including a wide range of applications such as travel mode choice, automobile ownership, or location decisions. However, the classical NL models are restricted by…

  • An invertible generative model for forward and inverse problems

    An invertible generative model for forward and inverse problems arXiv:2509.03910v1 Announce Type: new Abstract: We formulate the inverse problem in a Bayesian framework and aim to train a generative model that allows us to simulate (i.e., sample from the likelihood) and do inference (i.e., sample from the posterior). We review the use of triangular normalizing…

  • Towards Trustworthy Amortized Bayesian Model Comparison

    Towards Trustworthy Amortized Bayesian Model Comparison arXiv:2508.20614v1 Announce Type: new Abstract: Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. However, the reliability of neural surrogates deteriorates when simulation models are misspecified – the very case where model comparison is most needed. Thus, we supplement simulation-based training…

  • Comparing Model-agnostic Feature Selection Methods through Relative Efficiency

    Comparing Model-agnostic Feature Selection Methods through Relative Efficiency arXiv:2508.14268v1 Announce Type: new Abstract: Feature selection and importance estimation in a model-agnostic setting is an ongoing challenge of significant interest. Wrapper methods are commonly used because they are typically model-agnostic, even though they are computationally intensive. In this paper, we focus on feature selection methods related…

  • Noise Robust One-Class Intrusion Detection on Dynamic Graphs

    Noise Robust One-Class Intrusion Detection on Dynamic Graphs arXiv:2508.14192v1 Announce Type: cross Abstract: In the domain of network intrusion detection, robustness against contaminated and noisy data inputs remains a critical challenge. This study introduces a probabilistic version of the Temporal Graph Network Support Vector Data Description (TGN-SVDD) model, designed to enhance detection accuracy in the…

  • Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance

    Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance Automating model tuning in Python with Gemini, LangGraph, and Streamlit for regression and classification improvements The post Smarter Model Tuning: An AI Agent with LangGraph + Streamlit That Boosts ML Performance appeared first on Towards Data Science. Gustavo Santos Go to…

  • Preference Models assume Proportional Hazards of Utilities

    Preference Models assume Proportional Hazards of Utilities arXiv:2508.13189v1 Announce Type: new Abstract: Approaches for estimating preferences from human annotated data typically involves inducing a distribution over a ranked list of choices such as the Plackett-Luce model. Indeed, modern AI alignment tools such as Reward Modelling and Direct Preference Optimization are based on the statistical assumptions…

  • Help Your Model Learn the True Signal

    Help Your Model Learn the True Signal An algorithm-agnostic approach inspired by Cook’s distance The post Help Your Model Learn the True Signal appeared first on Towards Data Science. Mena Wang Go to original source

  • Model Predictive Control Basics

    Model Predictive Control Basics A hands-on tutorial with Python and CasADi The post Model Predictive Control Basics appeared first on Towards Data Science. Willem Esterhuizen Go to original source

  • Stochastic forest transition model dynamics and parameter estimation via deep learning

    Stochastic forest transition model dynamics and parameter estimation via deep learning arXiv:2507.21486v1 Announce Type: new Abstract: Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions…

  • Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification

    Deep Neural Network Driven Simulation Based Inference Method for Pole Position Estimation under Model Misspecification arXiv:2507.18824v1 Announce Type: cross Abstract: Simulation Based Inference (SBI) is shown to yield more accurate resonance parameter estimates than traditional chi-squared minimization in certain cases of model misspecification, demonstrated through a case study of pi-pi scattering and the rho(770) resonance.…

  • Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection

    Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection arXiv:2507.11136v1 Announce Type: new Abstract: Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model…

  • Do You Really Need a Foundation Model?

    Do You Really Need a Foundation Model? LLM or custom model: how should you choose the right solution? The post Do You Really Need a Foundation Model? appeared first on Towards Data Science. Vincent Vandenbussche Go to original source

  • Topic Model Labelling with LLMs

    Topic Model Labelling with LLMs Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The post Topic Model Labelling with LLMs appeared first on Towards Data Science. Petr Koráb Go to original source

  • Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation

    Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation arXiv:2507.08108v1 Announce Type: new Abstract: textit{Mallows model} is a widely-used probabilistic framework for learning from ranking data, with applications ranging from recommendation systems and voting to aligning language models with human preferences~cite{chen2024mallows, kleinberg2021algorithmic, rafailov2024direct}. Under this model, observed rankings are noisy perturbations of a…

  • Toto: A Foundation Time-Series Model Optimized for Observability Data

    Toto: A Foundation Time-Series Model Optimized for Observability Data Datadog open-sourced Toto (Time Series Optimized Transformer for Observability), a model purpose-built for observability data. Toto is currently the most extensively pretrained time-series foundation model: The pretraining corpus contains 2.36 trillion tokens, with ~70% coming from Datadog’s private telemetry dataset. Also, Toto currently ranks 2nd in…

  • An Introduction to Remote Model Context Protocol Servers

    An Introduction to Remote Model Context Protocol Servers Writing, testing and using them. The post An Introduction to Remote Model Context Protocol Servers appeared first on Towards Data Science. Thomas Reid Go to original source

  • Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work

    Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work Transforming Independent Models into Collaborative Intelligence The post Beyond Model Stacking: The Architecture Principles That Make Multimodal AI Systems Work appeared first on Towards Data Science. Eric Chung Go to original source

  • Online Conformal Model Selection for Nonstationary Time Series

    Online Conformal Model Selection for Nonstationary Time Series arXiv:2506.05544v1 Announce Type: new Abstract: This paper introduces the MPS (Model Prediction Set), a novel framework for online model selection for nonstationary time series. Classical model selection methods, such as information criteria and cross-validation, rely heavily on the stationarity assumption and often fail in dynamic environments which…

  • SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search

    SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search arXiv:2506.03657v1 Announce Type: new Abstract: Community detection is a fundamental task in graph analysis, with methods often relying on fitting models like the Stochastic Block Model (SBM) to observed networks. While many algorithms can accurately estimate SBM parameters when the input graph…

  • Your DNA Is a Machine Learning Model: It’s Already Out There

    Your DNA Is a Machine Learning Model: It’s Already Out There Even if you never sequenced your genome, predictive systems already know a lot about it. Genomic inference has become a population-scale model, and you’re probably in it. The post Your DNA Is a Machine Learning Model: It’s Already Out There appeared first on Towards…

  • How to Reduce Your Power BI Model Size by 90%

    How to Reduce Your Power BI Model Size by 90% Have you ever wondered what makes Power BI so fast and powerful when it comes to performance? Learn on a real-life example about data model optimization and general rules for reducing data model The post How to Reduce Your Power BI Model Size by 90%…

  • The Automation Trap: Why Low-Code AI Models Fail When You Scale

    The Automation Trap: Why Low-Code AI Models Fail When You Scale In the beginning, building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, low-code AI platforms have made things much easier now. Anyone can now directly make a model, link it to data, and publish it as…

  • Feature Representation Transferring to Lightweight Models via Perception Coherence

    Feature Representation Transferring to Lightweight Models via Perception Coherence arXiv:2505.06595v1 Announce Type: new Abstract: In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called textit{perception coherence}. Based on this notion, we propose a loss function, which takes into account…

  • Empowering LLMs to Think Deeper by Erasing Thoughts

    Empowering LLMs to Think Deeper by Erasing Thoughts Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called…

  • Model Compression: Make Your Machine Learning Models Lighter and Faster

    Model Compression: Make Your Machine Learning Models Lighter and Faster Introduction Whether you’re preparing for interviews or building Machine Learning systems at your job, model compression has become a must-have skill. In the era of LLMs, where models are getting larger and larger, the challenges around compressing these models to make them more efficient, smaller,…

  • A Step-By-Step Guide To Powering Your Application With LLMs

    A Step-By-Step Guide To Powering Your Application With LLMs You might be wondering whether GenAI is just hype or external noise. I also thought this was hype, and I could sit this one out until the dust cleared. Oh, boy, was I wrong. GenAI has real-world applications. It also generates revenue for companies, so we expect…

  • Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning

    Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning arXiv:2504.16192v1 Announce Type: cross Abstract: The continuous improvement in weather forecast skill over the past several decades is largely due to the increasing quantity of available satellite observations and their assimilation into operational forecast systems. Assimilating these observations requires observation operators in the form…

  • Explained: How Does L1 Regularization Perform Feature Selection?

    Explained: How Does L1 Regularization Perform Feature Selection? Feature Selection is the process of selecting an optimal subset of features from a given set of features; an optimal feature subset is the one which maximizes the performance of the model on the given task. Feature selection can be a manual or rather explicit process when…

  • On the Tunability of Random Survival Forests Model for Predictive Maintenance

    On the Tunability of Random Survival Forests Model for Predictive Maintenance arXiv:2504.14744v1 Announce Type: new Abstract: This paper investigates the tunability of the Random Survival Forest (RSF) model in predictive maintenance, where accurate time-to-failure estimation is crucial. Although RSF is widely used due to its flexibility and ability to handle censored data, its performance is…

  • Sesame  Speech Model:  How This Viral AI Model Generates Human-Like Speech

    Sesame  Speech Model:  How This Viral AI Model Generates Human-Like Speech Recently, Sesame AI published a demo of their latest Speech-to-Speech model. A conversational AI agent who is really good at speaking, they provide relevant answers, they speak with expressions, and honestly, they are just very fun and interactive to play with. Note that a…

  • How to Measure Real Model Accuracy When Labels Are Noisy

    How to Measure Real Model Accuracy When Labels Are Noisy Ground truth is never perfect. From scientific measurements to human annotations used to train deep learning models, ground truth always has some amount of errors. ImageNet, arguably the most well-curated image dataset has 0.3% errors in human annotations. Then, how can we evaluate predictive models…

  • Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines

    Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines arXiv:2504.05643v1 Announce Type: new Abstract: Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem with a complete dataset requires computing both data and model expectations and…

  • A Data Scientist’s Guide to Docker Containers

    A Data Scientist’s Guide to Docker Containers For a ML model to be useful it needs to run somewhere. This somewhere is most likely not your local machine. A not-so-good model that runs in a production environment is better than a perfect model that never leaves your local machine. However, the production machine is usually…

  • Unlock the Power of ROC Curves: Intuitive Insights for Better Model Evaluation

    Unlock the Power of ROC Curves: Intuitive Insights for Better Model Evaluation We’ve all been in that moment, right? Staring at a chart as if it’s some ancient script, wondering how we’re supposed to make sense of it all. That’s exactly how I felt when I was asked to explain the AUC for the ROC…

  • On Model Protection in Federated Learning against Eavesdropping Attacks

    On Model Protection in Federated Learning against Eavesdropping Attacks arXiv:2504.02114v1 Announce Type: cross Abstract: In this study, we investigate the protection offered by federated learning algorithms against eavesdropping adversaries. In our model, the adversary is capable of intercepting model updates transmitted from clients to the server, enabling it to create its own estimate of the…

  • The Art of Hybrid Architectures

    The Art of Hybrid Architectures In my previous article, I discussed how morphological feature extractors mimic the way biological experts visually assess images. This time, I want to go a step further and explore a new question:Can different architectures complement each other to build an AI that “sees” like an expert? Introduction: Rethinking Model Architecture…

  • Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures

    Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures arXiv:2503.17546v1 Announce Type: new Abstract: The behavior of multivariate dynamical processes is often governed by underlying structural connections that relate the components of the system. For example, brain activity which is often measured via time series is determined by an underlying structural graph, where…

  • Bayesian Kernel Regression for Functional Data

    Bayesian Kernel Regression for Functional Data arXiv:2503.13676v1 Announce Type: new Abstract: In supervised learning, the output variable to be predicted is often represented as a function, such as a spectrum or probability distribution. Despite its importance, functional output regression remains relatively unexplored. In this study, we propose a novel functional output regression model based on…

  • How to Spot and Prevent Model Drift Before it Impacts Your Business

    How to Spot and Prevent Model Drift Before it Impacts Your Business Despite the AI hype, many tech companies still rely heavily on machine learning to power critical applications, from personalized recommendations to fraud detection.  I’ve seen firsthand how undetected drifts can result in significant costs — missed fraud detection, lost revenue, and suboptimal business…

  • How to Train LLMs to “Think” (o1 & DeepSeek-R1)

    How to Train LLMs to “Think” (o1 & DeepSeek-R1) In September 2024, OpenAI released its o1 model, trained on large-scale reinforcement learning, giving it “advanced reasoning” capabilities. Unfortunately, the details of how they pulled this off were never shared publicly. Today, however, DeepSeek (an AI research lab) has replicated this reasoning behavior and published the…

  • Unraveling Large Language Model Hallucinations

    Unraveling Large Language Model Hallucinations Introduction In a YouTube video titled Deep Dive into LLMs like ChatGPT, former Senior Director of AI at Tesla, Andrej Karpathy discusses the psychology of Large Language Models (LLMs) as emergent cognitive effects of the training pipeline. This article is inspired by his explanation of LLM hallucinations and the information presented in the…

  • Debugging the Dreaded NaN

    Debugging the Dreaded NaN You are training your latest AI model, anxiously watching as the loss steadily decreases when suddenly — boom! Your logs are flooded with NaNs (Not a Number) — your model is irreparably corrupted and you’re left staring at your screen in despair. To make matters worse, the NaNs don’t appear consistently.…

  • How LLMs Work: Reinforcement Learning, RLHF, DeepSeek R1, OpenAI o1, AlphaGo

    How LLMs Work: Reinforcement Learning, RLHF, DeepSeek R1, OpenAI o1, AlphaGo Welcome to part 2 of my LLM deep dive. If you’ve not read Part 1, I highly encourage you to check it out first.  Previously, we covered the first two major stages of training an LLM: Pre-training — Learning from massive datasets to form a base…

  • Golden Ratio Mixing of Real and Synthetic Data for Stabilizing Generative Model Training

    Golden Ratio Mixing of Real and Synthetic Data for Stabilizing Generative Model Training arXiv:2502.18049v1 Announce Type: new Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation. Addressing this issue and developing more effective training strategies…

  • Tensor Product Neural Networks for Functional ANOVA Model

    Tensor Product Neural Networks for Functional ANOVA Model arXiv:2502.15215v1 Announce Type: new Abstract: Interpretability for machine learning models is becoming more and more important as machine learning models become more complex. The functional ANOVA model, which decomposes a high-dimensional function into a sum of lower dimensional functions so called components, is one of the most…

  • Model selection for behavioral learning data and applications to contextual bandits

    Model selection for behavioral learning data and applications to contextual bandits arXiv:2502.13186v1 Announce Type: new Abstract: Learning for animals or humans is the process that leads to behaviors better adapted to the environment. This process highly depends on the individual that learns and is usually observed only through the individual’s actions. This article presents ways…

  • Green LIME: Improving AI Explainability through Design of Experiments

    Green LIME: Improving AI Explainability through Design of Experiments arXiv:2502.12753v1 Announce Type: new Abstract: In artificial intelligence (AI), the complexity of many models and processes often surpasses human interpretability, making it challenging to understand why a specific prediction is made. This lack of transparency is particularly problematic in critical fields like healthcare, where trust in…

  • How to Fine-Tune DistilBERT for Emotion Classification

    How to Fine-Tune DistilBERT for Emotion Classification The customer support teams were drowning with the overwhelming volume of customer inquiries at every company I’ve worked at. Have you had similar experiences? What if I told you that you could use AI to automatically identify, categorize, and even resolve the most common issues? By fine-tuning a…

  • Learnings from a Machine Learning Engineer — Part 3: The Evaluation

    Learnings from a Machine Learning Engineer — Part 3: The Evaluation In this third part of my series, I will explore the evaluation process which is a critical piece that will lead to a cleaner data set and elevate your model performance. We will see the difference between evaluation of a trained model (one not yet in…

  • Learnings from a Machine Learning Engineer — Part 1: The Data

    Learnings from a Machine Learning Engineer — Part 1: The Data It is said that in order for a machine learning model to be successful, you need to have good data. While this is true (and pretty much obvious), it is extremely difficult to define, build, and sustain good data. Let me share with you…

  • Learnings from a Machine Learning Engineer — Part 4: The Model

    Learnings from a Machine Learning Engineer — Part 4: The Model In this latest part of my series, I will share what I have learned on selecting a model for Image Classification and how to fine tune that model. I will also show how you can leverage the model to accelerate your labelling process, and…

  • Understanding Model Calibration: A Gentle Introduction & Visual Exploration

    Understanding Model Calibration: A Gentle Introduction & Visual Exploration How Reliable Are Your Predictions? About To be considered reliable, a model must be calibrated so that its confidence in each decision closely reflects its true outcome. In this blog post we’ll take a look at the most commonly used definition for calibration and then dive…

  • A Visual Guide to How Diffusion Models Work

    A Visual Guide to How Diffusion Models Work This article is aimed at those who want to understand exactly how Diffusion Models work, with no prior knowledge expected. I’ve tried to use illustrations wherever possible to provide visual intuitions on each part of these models. I’ve kept mathematical notation and equations to a minimum, and where…

  • U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms

    U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms arXiv:2501.18084v1 Announce Type: new Abstract: Across various domains, the growing advocacy for open science and open-source machine learning has made an increasing number of models publicly available. These models allow practitioners to integrate them into their own contexts, reducing the need for extensive data labeling, training, and calibration.…

  • Building a Regression Model: Delivery Duration Prediction

    Building a Regression Model: Delivery Duration Prediction Building a Regression Model to Predict Delivery Durations: A Practical Guide E2E walkthrough for approaching a regression modeling task In this article, we’re going to walk through the process of building a regression model — from dataset cleaning & preparation, to model training & evaluation. The specific regression task we will…

  • Choosing Classification Model Evaluation Criteria

    Choosing Classification Model Evaluation Criteria Is Recall / Precision better than Sensitivity / Specificity? Continue reading on Towards Data Science » Viyaleta Apgar Go to original source

  • Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging

    Bayesian Model Parameter Learning in Linear Inverse Problems with Application in EEG Focal Source Imaging arXiv:2501.13109v1 Announce Type: cross Abstract: Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly. A physics-based forward model that relates the signal with the observations is typically needed. Unfortunately, unknown model…

  • Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression

    Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression arXiv:2501.10870v1 Announce Type: new Abstract: When concept shifts and sample scarcity are present in the target domain of interest, nonparametric regression learners often struggle to generalize effectively. The technique of transfer learning remedies these issues by leveraging data or pre-trained models from similar…

  • Large Language Models: A Short Introduction

    Large Language Models: A Short Introduction And why you should care about LLMs Image by author. There’s an acronym you’ve probably heard non-stop for the past few years: LLM, which stands for Large Language Model. In this article we’re going to take a brief look at what LLMs are, why they’re an extremely exciting piece of technology, why…

  • Zero-Shot Player Tracking in Tennis with Kalman Filtering

    Zero-Shot Player Tracking in Tennis with Kalman Filtering Automated tennis tracking without labels: GroundingDINO, Kalman filtering, and court homography https://medium.com/media/6f735abc63f905de122bb8a0679f97fd/href With the recent surge in sports tracking projects, many inspired by Skalski’s popular soccer tracking project, there’s been a notable shift towards using automated player tracking for sport hobbyists. Most of these approaches follow a…

  • Learnings from a Machine Learning Engineer — Part 4: The Model

    Learnings from a Machine Learning Engineer — Part 4: The Model Practical insights for a data-driven approach to model optimization Continue reading on Towards Data Science » David Martin Go to original source

  • On the use of Statistical Learning Theory for model selection in Structural Health Monitoring

    On the use of Statistical Learning Theory for model selection in Structural Health Monitoring arXiv:2501.08050v1 Announce Type: new Abstract: Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although…

  • rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

    rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms arXiv:2501.05772v1 Announce Type: cross Abstract: Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web…

  • Model Calibration, Explained: A Visual Guide with Code Examples for Beginners

    Model Calibration, Explained: A Visual Guide with Code Examples for Beginners MODEL EVALUATION & OPTIMIZATION When all models have similar accuracy, now what? You’ve trained several classification models, and they all seem to be performing well with high accuracy scores. Congratulations! But hold on — is one model truly better than the others? Accuracy alone doesn’t tell the…

  • Circuit Complexity Bounds for Visual Autoregressive Model

    Circuit Complexity Bounds for Visual Autoregressive Model arXiv:2501.04299v1 Announce Type: new Abstract: Understanding the expressive ability of a specific model is essential for grasping its capacity limitations. Recently, several studies have established circuit complexity bounds for Transformer architecture. Besides, the Visual AutoRegressive (VAR) model has risen to be a prominent method in the field of…

  • Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models

    Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models Full explanation on Linear Regression and how it learns The Crane Stance. Public Domain image from Openverse Just like Mr. Miyagi taught young Daniel LaRusso karate through repetitive simple chores, which ultimately transformed him into the Karate Kid, mastering foundational algorithms like linear regression…

  • Surrogate Modeling for Explainable Predictive Time Series Corrections

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