Wediscover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift.

High-dimensional compositional data, such as those from human microbiome studies, pose unique statistical challenges due to the simplex constraint and excess zeros. While dimension reduction is indispensable for analyzing such data, conventional approaches often rely on log-ratio transformations that compromise interpretability and distort the data through ad hoc zero replacements. We introduce a novel framework for interpretable dimension reduction of compositional data that avoids extra transformations and zero imputations. Our approach generalizes the concept of amalgamation by softening its operation, mapping high-dimensional compositions directly to a lower-dimensional simplex, which can be visualized in ternary plots. The framework further provides joint visualization of the reduction matrix, enabling intuitive, at-a-glance interpretation. To achieve optimal reduction within our framework, we incorporate sufficient dimension reduction, which defines a new identifiable objective: the central compositional subspace. For estimation, we propose a compositional kernel dimension reduction (CKDR) method. The estimator is provably consistent, exhibits sparsity that reveals underlying amalgamation structures, and comes with an intrinsic predictive model for downstream analyses. Applications to real microbiome datasets demonstrate that our approach provides a powerful graphical exploration tool for uncovering meaningful biological patterns, opening a new pathway for analyzing high-dimensional compositional data.

Machine learning (ML) is increasingly being used to support high-stakes decisions, a trend owed in part to its promise of superior predictive power relative to human assessment. However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models. As a result, machine learning models may fail to capture important dimensions of decision criteria, hampering their utility for decision support. In this work, we explore the use of historical expert decisions as a rich -- yet imperfect -- source of information that is commonly available in organizational information systems, and show that it can be leveraged to bridge the gap between decision objectives and algorithm objectives. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence function-based methodology as a solution to this problem. We then incorporate the estimated expert consistency into a predictive model through a training-time label amalgamation approach. This approach allows ML models to learn from experts when there is inferred expert consistency, and from observed labels otherwise. We also propose alternative ways of leveraging inferred consistency via hybrid and deferral models. In our empirical evaluation, focused on the context of child maltreatment hotline screenings, we show that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach significantly improves precision for these cases.

Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, most of these approaches are tailored for convolutional neural networks (CNNs). However, there is a tendency that transformers, with a completely different architecture, are starting to challenge the domination of CNNs in many computer vision tasks. Nevertheless, directly applying the previous KA methods to transformers leads to severe performance degradation. In this work, we explore a more effective KA scheme for transformer-based object detection models. Specifically, considering the architecture characteristics of transformers, we propose to dissolve the KA into two aspects: sequence-level amalgamation (SA) and task-level amalgamation (TA). In particular, a hint is generated within the sequence-level amalgamation by concatenating teacher sequences instead of redundantly aggregating them to a fixed-size one as previous KA works. Besides, the student learns heterogeneous detection tasks through soft targets with efficiency in the task-level amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that the sequence-level amalgamation significantly boosts the performance of students, while the previous methods impair the students. Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.

You've likely seen this before. ML engineer(s) organized these steps into a two stage end-to-end (e2e) pipeline. The first e2e pipeline consists of the first three steps, which is depicted in figure 1 below as modeling, data engineering, and training. Once the ML engineer(s) is successful with this stage, it would be coupled with the deployment step to form a second e2e pipeline. Typically, the model was deployed into a container environment and accessed via a REST based or microservice interface.

Automation is no longer a term confined to the dictionary of technical people only. Common folks are now very much aware of terms like Robotic Process Automation, Automation, Artificial Intelligence, and Machine Learning. To add to this list of leading technologies is Intelligent Process Automation. This article will give you a glimpse into what these technologies are, especially Intelligent Process Automation and Robotic Process Automation (RPA). So, let’s begin!

You can predict customer's sentiments. You can deliver a personalized experience to end-users. You can enhance business productivity. You can eliminate future risk. Welcome to the technology world where modern solutions and technologies such as Artificial Intelligence, the Internet of Things, and Machine Learning transform the business landscape, reduce repetitive tasks and minimize human error along the way.

The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature. While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence. Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks. The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI).

The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature. While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence. Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks. The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI).

Discreetly, stealthily, another kind of neuroscientist is coming to fruition. From inside the myriad positions of scholars have risen teams of neuroscientists that do science with information on neural activity, on the inadequate splutterings of many neurons. Not the production of techniques for analyzing data, however, all do that as well. Not the gathering of that information, for that, requires another, considerable, range of abilities. In any case, neuroscientists utilizing the full extent of modern computational strategies on that data to respond to scientific inquiries regarding the mind, neural data science has developed.