The disagreement is an open area of research in an interdisciplinary field known as Explainable Artificial Intelligence (XAI). Explainable Artificial Intelligence attempts to help us understand how our models are arriving at their decisions but unfortunately everything is easier said than done.
We are all aware that machine learning models and available datasets are growing larger and more complex. As a matter of fact, the data scientists who develop machine learning algorithms cannot
In the previous article, we discussed relational models which use attention mechanisms in their architecture. We used this model to create an Expert Advisor, and the resulting EA showed good results. However, we noticed that the model's learning rate was lower compared to our earlier experiments. This is due to the fact that the transformer block used in the model is a rather complex architectural solution performing a large number of operations. The number of these operations grows in a quadratic
In the realm of machine learning, more often than not we think in terms of trade offs. While optimising one metric of performance, we often compromise another performance metric. With the growing evolutionary trend of increasingly larger and more intricate models, understanding, explaining and debugging them become formidable tasks. The intricacies beneath the model's surface, deciphering 'why' our models are making the decisions they are making is vital. Without this clarity how can we confidently
more...Regression is a task of predicting a real value from an unlabeled example. A well-known example of regression is estimating the value of a diamond based on such characteristics as size, weight, color, clarity, etc.
The so-called regression metrics are used to assess the accuracy of regression model predictions. Despite similar algorithms, regression metrics are semantically different from similar loss functions.
This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent
Activity Information
Points
|
0 Achievements0 Awards0 Currently Held Trophies0 Previously Held Trophies |
Find latest posts
View Articles
View Blog Entries
Likes Received (29)
