Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense. Typically, the base hypothesis class is fixed to be all binary decision trees up to a given depth. In this work, we study a Heterogeneous Newton Boosting Machine (HNBM) in which the base hypothesis class may vary across boosting iterations. Specifically, at each boosting iteration, the base hypothesis class is chosen, from a fixed set of subclasses, by sampling from a probability distribution. We derive a global linear convergence rate for the HNBM under certain assumptions, and show that it agrees with existing rates for Newton's method when the Newton direction can be perfectly fitted by the base hypothesis at each boosting iteration. We then describe a particular realization of a HNBM, SnapBoost, that, at each boosting iteration, randomly selects between either a decision tree of variable depth or a linear regressor with random Fourier features. We describe how SnapBoost is implemented, with a focus on the training complexity. Finally, we present experimental results, using OpenML and Kaggle datasets, that show that SnapBoost is able to achieve better generalization loss than competing boosting frameworks, without taking significantly longer to tune.

When we want to understand how matter behaves, the real action happens at the atomic scale. Heating of water, a chemical reaction in a battery, the way proteins fold in our cells, or how a catalyst works to convert carbon dioxide into useful fuels, all of these processes are governed by the motions and interactions of atoms. Atomic-scale simulations give us a way to explore the microscopic behavior of matter, by tracking how atoms move under the laws of quantum mechanics. These simulations have become essential across physics, chemistry, biology, and materials science. They test hypotheses that experiments cannot easily probe and help design new materials before they are synthesized and tested in the lab.

Gear Home These De'Longhi coffee and espresso machine Prime Day deals are worth splurging on De'Longhi makes some of our very favorite coffee and espresso machines and these Prime Day deals have them at their lowest prices of the year. We may earn revenue from the products available on this page and participate in affiliate programs. My favorite takeout coffee costs more than $7 now each time I get it. The coffee itself has gotten more expensive, but there's also the tax and tip to consider. That's why investing in a fancy coffee machine makes more sense all the time.

The Best White-Noise Machines for a Blissful Night's Sleep Help the whole family catch more Z's with soothing background noise from our favorite sound machines. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. The Best White noise machine isn't a complex device, even as companies constantly add more bells and whistles. Nowadays, they come in all shapes and sizes, outfitted with the capacity to play other noise frequencies and nature sounds while at home or in a more portable, on-the-go form. They're not just for kids or babies anymore--if you're like us, trying to drown out your internal monologue so that you can finally drift off, this is the article for you. But if you're building up your arsenal of sleep gadgets, with a white noise machine among them, we've tried out everything from the best sleep trackers, best sunrise alarm clocks, the best mattresses, and the best extreme alarm clocks .

A suite of technologies are helping taxonomists speed up species identification. Listen to more stories on the Noa app. Across a Swiss meadow and into its forested edges, the drone dragged a jumbo-size cotton swab from a 13-foot tether. Along its path, the moistened swab collected scraps of life: some combination of sloughed skin and hair; mucus, saliva, and blood splatters; pollen flecks and fungal spores. Later, biologists used a sequencer about the size of a phone to stream the landscape's DNA into code, revealing dozens upon dozens of species, some endangered, some invasive.

Human-looking bipedal robots can already run, jump, breakdance, punch, and generally perform broad feats of athletic prowess most humans could only dream of. One thing they are still pretty bad at though is walking a straight line without looking like they are moments away from soiling themselves. Figure AI, one of the buzziest startups in the humanoid robot space, now says it has engineered a solution to help address their machine's stiff shuffle-step. The more natural-looking stride was achieved by analyzing thousands of virtual humanoid robots walking simultaneously in a simulated digital environment, Figure explained in a recent blog post. The company used reinforcement learning, rewarding the virtual robots for actions like synchronized arm swings, heel strikes, and toe-offs (when the toe leaves the ground) that more closely resemble human movement.

Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). Among all the models, the Gradient Boosting model demonstrated the highest performance and stability. Model explanation and feature importance analysis generated model interpretation that illustrated significant contributors associated with risks of stroke. Conclusions and Relevance: This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. The research results indicate that dynamic causal inference features have important value in predicting stroke risk, especially in capturing the impact of changes in health status over time on stroke risk. By further optimizing the model and introducing more variables, this study provides theoretical basis and practical guidance for future stroke prevention and intervention strategies.

Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an opportunity to harness these patterns for accurate disease diagnosis. Objective: This review synthesized findings from studies exploring ML's capability in leveraging speech for the diagnosis of neurological, laryngeal and mental disorders. Methods: A systematic examination of 564 articles was conducted with 91 articles included in the study, which encompassed a wide spectrum of conditions, ranging from voice pathologies to mental and neurological disorders. Methods for speech classifications were assessed based on the relevant studies and scored between 0-10 based on the reported diagnostic accuracy of their ML models. Results: High diagnostic accuracies were consistently observed for laryngeal disorders, dysarthria, and changes related to speech in Parkinsons disease. These findings indicate the robust potential of speech as a diagnostic tool. Disorders like depression, schizophrenia, mild cognitive impairment and Alzheimers dementia also demonstrated high accuracies, albeit with some variability across studies. Meanwhile, disorders like OCD and autism highlighted the need for more extensive research to ascertain the relationship between speech patterns and the respective conditions. Conclusion: ML models utilizing speech patterns demonstrate promising potential in diagnosing a range of mental, laryngeal, and neurological disorders. However, the efficacy varies across conditions, and further research is needed. The integration of these models into clinical practice could potentially revolutionize the evaluation and diagnosis of a number of different medical conditions.

This paper proposes an adaptive neural-compilation framework to address the problem of learning efficient programs. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target input distribution. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.

An article titled "On misbehaviour and fault tolerance in machine learning systems," by doctoral researcher Lalli Myllyaho was named one of the best papers in 2022 by the Journal of Systems and Software. "The fundamental idea of the study is that if you put critical systems in the hands of artificial intelligence and algorithms, you should also learn to prepare for their failure," Myllyaho says. It may not necessarily be dangerous if a streaming service suggests uninteresting options to users, but such behavior undermines trust in the functionality of the system. However, faults in more critical systems that rely on machine learning can be much more harmful. "I wanted to investigate how to prepare for, for example, computer vision misidentifying things. For instance, in computed tomography artificial intelligence can identify objects in sections. If errors occur, it raises questions about to what extent computers should be trusted in such matters, and when to ask a human to take a look," says Myllyaho.