wayr
[D] Machine Learning - WAYR (What Are You Reading) - Week 114
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
[D] Machine Learning - WAYR (What Are You Reading) - Week 113
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
[D] Machine Learning - WAYR (What Are You Reading) - Week 111
This paper by Arora, Ge, Neyshabur and Zhang proposes a compression based framework which purportedly explains the surprising generalization power of deep neural nets. The punchline is this - any neural network with certain robustness properties can be'compressed'. Compressed networks can be shown to generalize well, hence networks with these robustness properties are good candidates for networks that can hope to generalize well. The authors also show experimental evidence that these robustness properties are actually satisfied by real world neural nets. While I find the paper interesting, I am struggling with some of the technicalities.
[D] Machine Learning - WAYR (What Are You Reading) - Week 109
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
[D] Machine Learning - WAYR (What Are You Reading) - Week 100
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
[D] Machine Learning - WAYR (What Are You Reading) - Week 98
It'd be an overestimation from my side, but their architecture is kinda ingenious and we are trying to do it in our startup, using LM like BERT with position features to embed fields in the documents... It's more like semantically searching a document graph for a query label to detect... Off-the-shelf LMs don't have any mechanism to provide geometry information (that coming from my experience from using sentence-transformer). But, I think it can be done. I am moving in some direction at least. PS: Having tried GNN experiments, they didn't do well either for a highly imbalanced datasets where background nodes overshadow all the major labels.
[D] Machine Learning - WAYR (What Are You Reading) - Week 97
I've been focusing on my industry's market more than anything. I think ML at my company, plateaus around 5mil rows and scikit learn implemented random forests, logistic regression, KNN, and potentially NLP stuff .... nothing wrong with that, hell, I hate working with DNNs and'state of the art' bullshit that's out there. I'm focusing more on my actual industry and trends related to the new year. For example, in the hotel industry, our guests have primarily been staying due to business responsibilities, how and when do we expect the leisure market to act? Are they waiting for a vaccine?
[D] Machine Learning - WAYR (What Are You Reading) - Week 95
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.
[D] Machine Learning - WAYR (What Are You Reading) - Week 94
TL;DR This paper uses a technique called "iterative nullspace projection (INLP)" in order to debias word embedding representations. They do this by training linear classifiers to predict a certain attribute that they don't want the word embeddings to contain (e.g., race or gender information) and then project the weights of this classifier into the nullspace of the weight matrix. They perform this process iteratively in order to increasingly remove bias components. I personally found the paper really interesting because they took a bit of a different approach to typical NLP papers. One thing that I do find that needs work, however, is that their method does not fully address the "bias by neighbors" issue.
r/MachineLearning - [D] Machine Learning - WAYR (What Are You Reading) - Week 84
This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.