If you’re working with time series data, you know how crucial it is to identify structural breaks – points where […]
Detecting Structural Breaks in Time Series Data: A Challenge Read More »
As machine learning continues to evolve, feature engineering remains a crucial aspect of building effective models. One area that has
Unlocking the Power of Orthogonal Bases in Machine Learning Read More »
I’m thrilled to share my experience using GEPA to optimize a Listwise Reranker, and I’m excited to help you get
Unlocking the Power of GEPA: Optimizing Listwise Reranking with Prompt Optimization Read More »
When working with gesture recognition datasets, we often encounter a common problem: dealing with variable-length sensor sequences. This is exactly
Handling Variable-Length Sensor Sequences in Gesture Recognition: To Pad or Not to Pad? Read More »
Have you ever spent hours labeling text datasets, only to feel like you’re stuck in a never-ending cycle of monotony?
The Pain of Text Labeling: How to Make It More Efficient Read More »
Have you ever felt lost in a sea of technical jargon, wondering how someone could make something so… simple? That’s
Break Down Complex Concepts with ELI5: The Power of Simple Explanations Read More »
When working with non-linear rational features, fitting a linear model can be a challenge. Think of features like ‘time since
The Quest for Rational Features: Where to Place the Poles? Read More »
If you’re in the machine learning community, you’re probably familiar with the ACL Rolling Review (ARR) process for EMNLP conferences.
Cracking the Code: Understanding ACL Rolling Review Stats for EMNLP 2025 Read More »
I’m sure many of you can relate to the feeling of being overwhelmed by a sea of data, especially when
Unraveling Time Series Data for Root Cause Analysis (RCA) Read More »
I still remember the thrill of submitting my first deep learning paper. It was a journey that lasted almost two
My Journey to Submitting a Deep Learning Paper: Lessons Learned Read More »