Furthermore, their guarantees only hold in the realizable setting, requiring thatf is itself a size-s decision tree (i.e.opts = 0). There has been extensive work in the learning theory literature on learning the concept class of decision trees [EH89, Blu92, KM93, OS07, GKK08, HKY18, CM19]. This follows by combining the boundsInf(T) logs (see e.g.

Mobile-centric AI applications have high requirements for resource-efficiency of model inference. Input filtering is a promising approach to eliminate the redundancy so as to reduce the cost of inference. Previous efforts have tailored effective solutions for many applications, but left two essential questions unanswered: (1) theoretical filterability of an inference workload to guide the application of input filtering techniques, thereby avoiding the trial-and-error cost for resource-constrained mobile applications; (2) robust discriminability of feature embedding to allow input filtering to be widely effective for diverse inference tasks and input content. To answer them, we first formalize the input filtering problem and theoretically compare the hypothesis complexity of inference models and input filters to understand the optimization potential. Then we propose the first end-to-end learnable input filtering framework that covers most state-of-the-art methods and surpasses them in feature embedding with robust discriminability. We design and implement InFi that supports six input modalities and multiple mobile-centric deployments. Comprehensive evaluations confirm our theoretical results and show that InFi outperforms strong baselines in applicability, accuracy, and efficiency. InFi achieve 8.5x throughput and save 95% bandwidth, while keeping over 90% accuracy, for a video analytics application on mobile platforms.

Since the start of the 20th century, multiple psychological models have emerged to provide a scientific explanation for human motivation and a better understanding of why people do what they do. From the five-factor model, measuring traits like extraversion, agreeableness and neuroticism, or the HEXACO personality analysis of honesty, humility, and emotionality, among other traits, these models have stood the test of time in explaining the complexities of the convoluted human behavior and its mysterious makeup. But as the world has increasingly digitized, there's a new source of behavioural activity we leave behind that provides a better reflection of ourselves than previously thought. Enter INFI, a game-changer in the space of Artificial Intelligence (AI) and human-machine interaction, and the first to crack the code of converting validated psychological models into algorithms, enabling a new level of individually-based insights. Their technology promises to reinvent a lost art of personal communication and change the way people are engaged in the digital world.