Customer-obsessed science

With increasing scale in model and dataset size, the training of deep neural networks becomes a massive computational burden. One approach to speed up the training process is Selective Backprop. For this approach, we perform a forward pass to obtain a loss value for each data point in a minibatch. The backward pass is then restricted to a subset of that minibatch, prioritizing high-loss examples. We build

Most linear experimental design problems assume homogeneous variance, even though heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set of measurement vectors X ⊂ ℝd that can be probed to receive noisy linear responses of the form y = x⊤θ* + η. Here θ* ∈ ℝd is an unknown parameter vector, and η is independent mean-zero σx2 -strictly-sub-Gaussian noise defined

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage

To build scalable models for the challenging real-world tasks, it is important to learn from diverse, multi-modal data in various forms (e.g., videos, text, images). Amongst the existing works, a plethora of them have been focusing on leveraging large but cumbersome cross-modal architectures. Regardless of their effectiveness, larger architectures unavoidably prevent the models from being extended to realworld

The Net Promoter Score (NPS) is often used in customer experience programs for measuring customer loyalty. Increasingly more companies seek to automatically process millions of pieces of customer feedback from social media per month in order to estimate their NPS, leveraging advanced analytics like machine learning (ML) and natural language processing (NLP). Discovering trends and themes in customer interactions