分类： Machine Learning

Why Causality Matters in Machine Learning: Moving Beyond Correlation

I’ve been working with machine learning systems for a while now, and I’ve noticed a common problem. Models that look great on paper often fail in real-world production because they focus on correlations rather than causal mechanisms. This is a big deal, because if your model is just finding patterns in the data, it might not actually be able to predict what will happen in the future or make good decisions.

Let me give you an example. Imagine you’re building a model to diagnose plant diseases. Your model can predict the disease with 90% accuracy, but if it’s just looking at correlations, it might give you recommendations that actually make things worse. That’s because prediction isn’t the same as intervention. Just because your model can predict what’s happening doesn’t mean it knows how to fix it.

So, what’s the solution? It’s to build models that understand causality. This means looking at the underlying mechanisms that drive the data, rather than just the patterns in the data itself. It’s a harder problem, but it’s also a more important one.

I’ve been exploring this topic in a series of blog posts, where I dive into the details of building causal machine learning systems. I cover topics like Pearl’s Ladder of Causation, which is a framework for understanding the different levels of causality. I also look at practical implications, like when you need to use causal models and when correlation is enough.

One of the key insights from this work is that your model can be really good at predicting something, but still give you bad advice. That’s because prediction and intervention are different things. To build models that can actually make good decisions, you need to focus on causality.

If you’re interested in learning more, I’d recommend checking out my blog series. It’s a deep dive into the world of causal machine learning, but it’s also accessible to anyone who’s interested in the topic. And if you have any thoughts or questions, I’d love to hear them.

2026年1月13日
Struggling to Understand Machine Learning Papers? You’re Not Alone

Hey, have you ever found yourself stuck on a machine learning research paper, wondering what the authors are trying to say? You’re not alone. I’ve been there too, and it can be really frustrating. That’s why I was interested to see a recent post on Reddit where someone was looking for people who struggle with ML papers. They’re working on a free solution to help make these papers more accessible, and they want feedback from people like us.

It’s great to see people working on solutions to help others understand complex topics like machine learning. Reading research papers can be tough, even for experienced professionals. The language is often technical, and the concepts can be difficult to grasp. But with the right tools and resources, it can get a lot easier.

So, what can we do to make ML papers more accessible? For starters, we can look for resources like blogs, videos, and podcasts that explain complex concepts in simpler terms. We can also join online communities, like the one on Reddit, where we can ask questions and get feedback from others who are going through the same thing.

If you’re struggling with ML papers, don’t be afraid to reach out for help. There are people out there who want to support you, and there are resources available to make it easier. And who knows, you might even find a solution that makes reading research papers enjoyable.

2026年1月5日
A Treasure Trove of Plant Images: 96.1M Rows of iNaturalist Research-Grade Data

I recently stumbled upon an incredible dataset of plant images on Reddit. It’s a massive collection of 96.1M rows of iNaturalist Research-Grade plant images, complete with species names, coordinates, licenses, and more. The best part? It’s been carefully cleaned and packed into a Hugging Face dataset, making it easier to use for machine learning projects.

The creator of the dataset, /u/Lonely-Marzipan-9473, was working with GBIF (Global Biodiversity Information Facility) data and found it to be messy and difficult to use for ML. They decided to take matters into their own hands and create a more usable dataset.

The dataset is a plant subset of the iNaturalist Research Grade Dataset and includes images, species names, coordinates, licenses, and filters to remove broken media. It’s a great resource for anyone looking to test vision models on real-world, noisy data.

What’s even more impressive is that the creator also fine-tuned Google Vit Base on 2M data points and 14k species classes. You can find the model on Hugging Face, along with the dataset.

If you’re interested in plant identification or machine learning, this dataset is definitely worth checking out. And if you have any questions or feedback, the creator is happy to hear from you.

2025年12月6日
The Truth About Training Production-Level Models

Hey, have you ever wondered how big tech companies train their production-level models? I mean, think about it – training these models can be super costly. So, do researchers log test set results when training these models? Or do they use something like reinforcement learning (RL) with feedback from the test sets?

It’s a pretty interesting question, and one that I’ve been thinking about a lot lately. I mean, when you’re dealing with huge datasets and complex models, it can be tough to know exactly what’s going on under the hood. But, if we can get a better understanding of how these models are trained, we might be able to make them even more effective.

From what I’ve learned, it seems like there are a few different approaches that researchers use. Some might use techniques like cross-validation to get a sense of how well their model is performing on unseen data. Others might use more advanced methods, like Bayesian optimization, to tune their model’s hyperparameters.

But, here’s the thing: it’s not always easy to get a clear answer about what’s going on. I mean, these companies are often working on super sensitive projects, and they might not be willing to share all the details. So, we’re kind of left to piece together what we can from research papers and blog posts.

So, what do you think? How do you think big tech companies should be training their production-level models? Should they be using more transparent methods, or is it okay for them to keep some things under wraps?

Some things to consider:
* How do researchers currently log test set results, and what are the benefits and drawbacks of this approach?
* What role does reinforcement learning play in training production-level models, and how can it be used effectively?
* What are some potential pitfalls or challenges that researchers might face when training these models, and how can they be addressed?

I’m curious to hear your thoughts on this – let me know what you think!

2025年11月17日
Finding Your Perfect Match: Choosing a Thesis Topic in Machine Learning

Hey, if you’re like me, you’re probably excited but also a bit overwhelmed when it comes to choosing a thesis topic in machine learning. It’s a big decision, and you want to make sure you pick something that’s both interesting and manageable. So, how do you decide on a thesis topic?

For me, it started with exploring different areas of machine learning, like computer vision, natural language processing, or reinforcement learning. I thought about what problems I wanted to solve and what kind of impact I wanted to make. Did I want to work on something that could help people, like medical imaging or self-driving cars? Or did I want to explore more theoretical concepts, like adversarial attacks or explainability?

One approach is to start by looking at existing research papers or projects and seeing if you can build upon them or identify gaps that need to be filled. You could also browse through datasets and think about how you could use them to answer interesting questions or solve real-world problems. Another option is to talk to your academic guide or other experts in the field and get their input on potential topics.

If you’re interested in computer vision like I am, you could explore topics like object detection, image segmentation, or generative models. You could also look into applications like facial recognition, surveillance, or medical imaging. The key is to find something that aligns with your interests and skills, and that has the potential to make a meaningful contribution to the field.

Some tips that might help you in your search:
* Read research papers and articles to stay up-to-date with the latest developments in machine learning
* Explore different datasets and think about how you could use them to answer interesting questions
* Talk to experts in the field and get their input on potential topics
* Consider what kind of impact you want to make and what problems you want to solve

I hope this helps, and I wish you the best of luck in finding your perfect thesis topic!

2025年11月11日
Unlocking the Power of Triplets: A GPU-Accelerated Approach

I’ve always been fascinated by the potential of triplets in natural language processing. Recently, I stumbled upon an open-source project that caught my attention – a Python port of Stanford OpenIE, with a twist: it’s GPU-accelerated using spaCy. What’s impressive is that this approach doesn’t rely on trained neural models, but instead accelerates the natural-logic forward-entailment search itself. The result? More triplets than standard OpenIE, while maintaining good semantics.

The project’s focus on retaining semantic context for applications like GraphRAG, embedded queries, and scientific knowledge graphs is particularly interesting. It highlights the importance of preserving the meaning and relationships between entities in text. By leveraging GPU acceleration, this project demonstrates the potential for significant performance gains in triplet extraction.

If you’re curious about the details, the project is available on GitHub. It’s a great example of how innovation in NLP can lead to more efficient and effective solutions. So, what do you think? Can GPU-accelerated triplet extraction be a game-changer for your NLP projects?

Some potential applications of this technology include:
* Improved question answering systems
* Enhanced entity recognition and disambiguation
* More accurate information extraction from text
* Better support for natural language interfaces

2025年11月4日
PKBoost: A New Gradient Boosting Method That Stays Accurate Under Data Drift

I recently came across a Reddit post about a new gradient boosting implementation called PKBoost. The author had been working on this project to address two common issues they faced with XGBoost and LightGBM in production: performance collapse on extremely imbalanced data and silent degradation when data drifts.

The key results showed that PKBoost outperformed XGBoost and LightGBM on imbalanced data, with an impressive 87.8% PR-AUC on the Credit Card Fraud dataset. But what’s even more interesting is how PKBoost handled data drift. Under realistic drift scenarios, PKBoost experienced only a 2% degradation in performance, whereas XGBoost saw a whopping 32% degradation.

So, what makes PKBoost different? The main innovation is the use of Shannon entropy in the split criterion alongside gradients. This approach explicitly optimizes for information gain on the minority class, which helps to prevent overfitting to the majority class. Combined with quantile-based binning, conservative regularization, and PR-AUC early stopping, PKBoost is inherently more robust to drift without needing online adaptation.

While PKBoost has its trade-offs, such as being 2-4x slower in training, its ability to auto-tune for your data and work out-of-the-box on extreme imbalance makes it an attractive option for production systems. The author is looking for feedback on whether others have seen similar robustness from conservative regularization and whether this approach would be useful for production systems despite the slower training times.

2025年10月27日
Running ONNX AI Models with Clojure: A New Era for Machine Learning

Hey, have you heard about the latest development in the Clojure world? It’s now possible to run ONNX AI models directly in Clojure. This is a big deal for machine learning enthusiasts and developers who work with Clojure.

For those who might not know, ONNX (Open Neural Network Exchange) is an open format used to represent trained machine learning models. It allows models to be transferred between different frameworks and platforms, making it a crucial tool for deploying AI models in various environments.

The ability to run ONNX models in Clojure means that developers can now leverage the power of machine learning in their Clojure applications. This could lead to some exciting innovations, from natural language processing to image recognition and more.

But what does this mean for you? If you’re a Clojure developer, you can now integrate machine learning into your projects without having to leave the comfort of your favorite programming language. And if you’re an AI enthusiast, you can explore the possibilities of ONNX models in a new and powerful ecosystem.

To learn more about this development and how to get started with running ONNX models in Clojure, you can check out the article by Dragan Djordjevic, which provides a detailed overview of the process and its implications.

2025年10月27日
How to Cut Inference Costs by 84% with Qwen-Image-Edit

So, you’re working with large datasets and need to generate a ton of images. I recently came across a story about optimizing Qwen-Image-Edit, an open-source model, to reduce inference costs dramatically. The goal was to create a product catalogue of 1.2 million images, which initially would have cost $46,000 with other models like Nano-Banana or GPT-Image-Edit.

The team decided to fine-tune Qwen-Image-Edit, taking advantage of its Apache 2.0 license. They applied several techniques like compilation, lightning LoRA, and quantization to cut costs. The results were impressive: they reduced the inference time from 15 seconds per image to just 4 seconds.

But what does this mean in terms of costs? Initially, generating all the images would have required 5,000 compute hours. After optimization, this number decreased to approximately 1,333 compute hours, resulting in a cost reduction from $46,000 to $7,500. That’s an 84% decrease in costs.

I think this story highlights the importance of exploring open-source models and fine-tuning them for specific tasks. By doing so, you can significantly reduce your costs and make your workflow more efficient. If you’re curious about the details, you can find more information on the Oxen.ai blog, where they shared their experience with Qwen-Image-Edit.

It’s always exciting to see how machine learning models can be optimized and used in real-world applications. This story is a great example of how fine-tuning and cost optimization can make a big difference in the industry.

2025年10月27日
Exploring OpenEnv: A New Era for Reinforcement Learning in PyTorch

I recently stumbled upon OpenEnv, a framework that’s making waves in the reinforcement learning (RL) community. For those who might not know, RL is a subset of machine learning that focuses on training agents to make decisions in complex environments. OpenEnv aims to simplify the process of creating and training these agents, and it’s built on top of PyTorch, a popular deep learning library.

So, what makes OpenEnv special? It provides a set of pre-built environments that can be used to train RL agents. These environments are designed to mimic real-world scenarios, making it easier to develop and test agents that can navigate and interact with their surroundings. The goal is to create agents that can learn from their experiences and adapt to new situations, much like humans do.

One of the key benefits of OpenEnv is its flexibility. It allows developers to create custom environments tailored to their specific needs, which can be a huge time-saver. Imagine being able to train an agent to play a game or navigate a virtual world without having to start from scratch. That’s the kind of power that OpenEnv puts in your hands.

If you’re interested in learning more about OpenEnv and its potential applications, I recommend checking out the official blog post, which provides a detailed introduction to the framework and its capabilities. You can also explore the OpenEnv repository on GitHub, where you’ll find documentation, tutorials, and example code to get you started.

Some potential use cases for OpenEnv include:

* Training agents to play complex games like chess or Go
* Developing autonomous vehicles that can navigate real-world environments
* Creating personalized recommendation systems that can adapt to user behavior

These are just a few examples, but the possibilities are endless. As the RL community continues to grow and evolve, it’s exciting to think about the kinds of innovations that OpenEnv could enable.

What do you think about OpenEnv and its potential impact on the RL community? I’d love to hear your thoughts and discuss the possibilities.

2025年10月27日