The increasing amount of available data and more affordable hardware solutions have opened a gate to the realm of Deep Learning (DL). Due to the rapid advancements and ever-growing popularity of DL, it has begun to invade almost every field, where machine learning is applicable, by altering the traditional state-of-the-art methods… While many researchers in the speaker recognition area have also started to replace the former state-of-the-art methods with DL techniques, some of the traditional i-vector-based methods are still state-of-the-art […]

In this work, we introduce a novel task – Humancentric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatiotemporal tube of the target person from an untrimmed video based on a given textural description… This task is useful, especially for healthcare and security-related applications, where the surveillance videos can be extremely long but only a specific person during a specific period of time is concerned. […]

Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications. Yet, current methods including aggregation-based and alignment-based fusion are still inadequate in balancing the trade-off between inter-modal fusion and intra-modal processing, incurring a bottleneck of performance improvement… To this end, this paper proposes Channel-Exchanging-Network (CEN), a parameter-free multimodal fusion framework that dynamically exchanges channels between sub-networks of different modalities. Specifically, the channel exchanging process is […]

Interpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a trade-off: either the models are too constrained to be very useful, or the solutions found by the models are too complex to interpret. We propose a novel strategy for achieving interpretability that — in our experiments — avoids this trade-off… Our approach builds on the success of using probability as the […]

A domain specific language (DSL), named MotePy is presented. The DSL offers a high level syntax with low overheads for ML/data processing in time constrained or memory constrained systems… The DSL-to-C compiler has a novel static memory allocator that tracks object lifetimes and reuses the static memory, which we call the compiler-managed heap. (read more) PDF Abstract  

The quality of healthcare provided by hospitals is subject to considerable variability. Consequently, accurate measurements of hospital performance are essential for various decision-makers, including patients, hospital managers and health insurers… Hospital performance is assessed via the health outcomes of their patients. However, as the risk profiles of patients between hospitals vary, measuring hospital performance requires adjustment for patient risk. This task is formalized in the state-of-the-art procedure through a hierarchical generalized linear model, that isolates hospital fixed-effects from the effect […]

Social coding platforms, such as GitHub, serve as laboratories for studying collaborative problem solving in open source software development; a key feature is their ability to support issue reporting which is used by teams to discuss tasks and ideas. Analyzing the dialogue between team members, as expressed in issue comments, can yield important insights about the performance of virtual teams… This paper presents a transfer learning approach for performing dialogue act classification on issue comments. Since no large labeled corpus […]

NLP is currently dominated by general-purpose pretrained language models like RoBERTa, which achieve strong performance on NLU tasks through pretraining on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data?.. We adopt four probing methods—classifier probing, information-theoretic probing, unsupervised relative acceptability judgment, and fine-tuning on NLU tasks—and draw learning curves that track the growth of these different measures of linguistic ability with respect to pretraining data […]