Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
So, my PhD topic is to implement transformer models for toxicity prediction and molecular generation. Or in plain english, given a molecule (e.g., a drug), I want to train an “AI” model to predict its possible toxic effect on the body and the environment. Even better, create new formulas for molecules that will be safe biologically and environmentally. However, in this post, I will show you how I had to make a major detour before I even attempt to do so!
Published:
In the last technical post, I talked about how to tell when one is in a state to start comparing models. I found that I needed to satisfy some conditions before concluding that my model is suitable for my data and representation. Now, assuming that I have such a suitable model and I want to compare it to other suitable models—or I found no such model, and I just want to see which of my suboptimal models is the least suboptimal—Is cross-validation the next logical step?
Short answer: Not as we use it today!
Published:
In this post, I go back to the “How Good is My Model?” lane and continue the journey. However, since the next stop is the “Cross-Validation land,” which in my field is mainly about model comparison, one needs to go through this sanity check before moving to comparison. This check should indicate whether I am ready to start comparing models—or not yet.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.
Published:
In the last post, I discussed how a model’s performance is more faithfully reported as a distribution rather than a single value. Also, how it’s important to report it mindfully to distinguish between what is typical and what is expected, and how standard deviation (σ) can quantify a model’s stability. However, I also discussed that one distribution from a small test set is not guaranteed to give an overview of the model’s true distribution. In this post, I’ll discuss how to move forward from this bottleneck.
Published:
In my first blog post, I made two bold statements about transformer models for molecular property prediction in our review1 — namely, that they fall short in terms of both novelty and benchmarking. In this and the following posts, I hope to convince you of these conclusions.
Published:
In the last two posts, I was trying to answer a question but found that it only became entangled with the limitations of reality. To know how good a model is, I need to land on the true distribution of its performance—to see what is common and what is rare. However, this requires a large number of i.i.d. samples, which unfortunately are not easy to find in our field.