Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be understandable to a wide range of stakeholders. Methods to explain AI have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes any XAI method into four components: (1) the inference to be explained, (2) the explanatory medium, (3) the explainee model, and (4) the explainer model. The abstraction afforded by Bayesian Teaching to decompose any XAI method elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi-independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real-world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI.

From our reading, we can conclude that Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. We need not know what is statistics or linear algebra to master in Linear Regression. In this post, we have discovered its meaning in a layman's understanding, and have checked out its benefits and some real-life examples. We have also covered the two types of Linear Regression algorithms and their implementation using Python. I hope this blog has provided my readers some basic knowledge to be able to solve any regression problems effectively.

Let us go through one whole step to explain what is happening. At t 1 we're going to use decoder state s_t 1 to computer alignment scores. To compute the alignment score for every encoder state we're using a function that is called alignment function but it's just an MLP (MultiLayer Perceptron). Each alignment score can be treated as "how much h1 is useful in predicting the output in the state s0". The alignment function outputs a scalar value which is a real number and we cannot use it just like that, we have to normalize those values using the softmax function. Output from the softmax function is normalized so all the numbers sum up to 1.

To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with local averaging. In LocalNewton, the worker machines update their model in every iteration by finding a suitable second-order descent direction using only the data and model stored in their own local memory. We let the workers run multiple such iterations locally and communicate the models to the master node only once every few (say L) iterations. LocalNewton is highly practical since it requires only one hyperparameter, the number L of local iterations. We use novel matrix concentration-based techniques to obtain theoretical guarantees for LocalNewton, and we validate them with detailed empirical evaluation. To enhance practicability, we devise an adaptive scheme to choose L, and we show that this reduces the number of local iterations in worker machines between two model synchronizations as the training proceeds, successively refining the model quality at the master. Via extensive experiments using several real-world datasets with AWS Lambda workers and an AWS EC2 master, we show that LocalNewton requires fewer than 60% of the communication rounds (between master and workers) and less than 40% of the end-to-end running time, compared to state-of-the-art algorithms, to reach the same training~loss.

Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness against the class-conditional noise. However, they typically focus on the single label case by assuming that only one label is corrupted. In real applications, an instance is usually associated with multiple labels, which could be corrupted simultaneously with their respective conditional probabilities. In this paper, we formalize this problem as a general framework of learning with Class-Conditional Multi-label Noise (CCMN for short). We establish two unbiased estimators with error bounds for solving the CCMN problems, and further prove that they are consistent with commonly used multi-label loss functions. Finally, a new method for partial multi-label learning is implemented with unbiased estimator under the CCMN framework. Empirical studies on multiple datasets and various evaluation metrics validate the effectiveness of the proposed method.

In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.

The inputs of the final layer are n-dimensional high-level feature vectors representing the data. The layer performs two operations: 1. a linear transformation of the input, 2. conversion of the learned features into prediction probabilities.

Mixture of experts is an ensemble learning technique developed in the field of neural networks. It involves decomposing predictive modeling tasks into sub-tasks, training an expert model on each, developing a gating model that learns which expert to trust based on the input to be predicted, and combines the predictions. Although the technique was initially described using neural network experts and gating models, it can be generalized to use models of any type. As such, it shows a strong similarity to stacked generalization and belongs to the class of ensemble learning methods referred to as meta-learning. In this tutorial, you will discover the mixture of experts approach to ensemble learning.

Python is one of the most widely used programming languages in the data science field. Python has many packages and libraries that are specifically tailored for certain functions, including pandas, NumPy, scikit-learn, Matplotlib, and SciPy. So if you are looking for the Best Books on Data Science with Python, then you should check these books. In this article, you will find 8 Best Books on Data Science with Python. These books will give you in-depth knowledge starting from basics to advanced level.

Have you ever asked yourself where you currently are on your Machine Learning journey? And what’s there that you can still learn about? This checklist helps you answer such questions. It provides an…