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 […]

Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain, in terms of both medical understanding and system performance, remains bounded by the availability of adequate data… Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that […]

In this paper, we propose textit{selectively precoded polar (SPP) code}, built on top of Arikan’s capacity achieving polar codes. We provide the encoding and decoding scheme for SPP code… Simulation results show that for a target frame erasure rate (FER) of $10^{-5}$, a (128, 64) SPP code is just 0.23 dB away from the information theoretic limit at this blocklength. Further, it is also shown that such codes have very good distance properties compared to other contemporary polar code variants. […]

Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context. For Chinese MRC, due to the non-literal and non-compositional semantic characteristics, Chinese idioms pose unique challenges for machines to understand… Previous studies tend to treat idioms separately without fully exploiting the relationship among them. In this paper, we first define the concept of literal meaning coverage to measure the consistency between semantics and literal meanings for Chinese idioms. With the definition, […]

In practice, it is quite common to face combinatorial optimization problems which contain uncertainty along with non-determinism and dynamicity. These three properties call for appropriate algorithms; reinforcement learning (RL) is dealing with them in a very natural way… Today, despite some efforts, most real-life combinatorial optimization problems remain out of the reach of reinforcement learning algorithms. In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in […]

In this paper we introduce EfficientPose, a new approach for 6D object pose estimation. Our method is highly accurate, efficient and scalable over a wide range of computational resources… Moreover, it can detect the 2D bounding box of multiple objects and instances as well as estimate their full 6D poses in a single shot. This eliminates the significant increase in runtime when dealing with multiple objects other approaches suffer from. These approaches aim to first detect 2D targets, e.g. keypoints, […]