This paper develops a new approach for extremely fast detection in do- mains where the distribution of positive and negative examples is highly skewed (e.g. In such domains a cascade of simple classifiers each trained to achieve high detection rates and modest false positive rates can yield a final detector with many desir- able features: including high detection rates, very low false positive rates, and fast performance. Achieving extremely high detection rates, rather than low error, is not a task typically addressed by machine learning al- gorithms. We propose a new variant of AdaBoost as a mechanism for training the simple classifiers used in the cascade. The final face detection system can process 15 frames per second, achieves over 90% detection, and a false positive rate of 1 in a 1,000,000.

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation. Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.

Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-of-distribution (OOD) samples nearly as confidently as in-distribution samples, making them indistinguishable from each other. To tackle this challenge, some recent works have demonstrated the gains of leveraging readily accessible OOD sets for training end-to-end calibrated CNNs. However, a critical question remains unanswered in these works: how to select an OOD set, among the available OOD sets, for training such CNNs that induces high detection rates on unseen OOD sets? We address this pivotal question through the use of Augmented-CNN (A-CNN) involving an explicit rejection option. We first provide a formal definition to precisely differentiate OOD sets for the purpose of selection. As using this definition incurs a huge computational cost, we propose novel metrics, as a computationally efficient tool, for characterizing OOD sets in order to select the proper one. In a series of experiments on several image and audio benchmarks, we show that training an A-CNN with an OOD set identified by our metrics (called A-CNN$^{\star}$) leads to remarkable detection rate of unseen OOD sets while maintaining in-distribution generalization performance, thus demonstrating the viability of our metrics for identifying the proper OOD set. Furthermore, we show that A-CNN$^{\star}$ outperforms state-of-the-art OOD detectors across different benchmarks.

Artificial intelligence and machine learning have become key drivers of innovation. Machine-learning algorithms significantly improve detection time for modern threats, as they can analyze large amounts of data significantly faster than any human could. If trained to accurately detect various types of malware behavior, machine-learning algorithms can have a high detection rate, even on new or unknown samples. The merging of human ingenuity with the speed and relentless data analysis of machine learning significantly accelerates reactions against new malware, offering protection even from previously unknown samples – advanced persistent threats, zero-day attacks, and ransomware. Detecting ransomware, for example, requires several algorithms, each specialized in detecting specific families with individual behaviors.

This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g.

This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g.

This paper develops a new approach for extremely fast detection in domains wherethe distribution of positive and negative examples is highly skewed (e.g.