DLocRL
In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. Prior studies heavily rely on hand-crafted linguistic features. Recently, deep learning has shown its effectiveness in feature representation learning for many NLP tasks. In this paper, we propose DLocRL, a new Deep pipeline for fine-grained Location Recognition and Linking in tweets. DLocRL leverages representation learning, semantic composition, attention and gate mechanisms to exploit semantic context features for location recognition and linking. Furthermore, a novel post-processing strategy, named Geographical Pair Linking, is developed to improve the linking performance. In this way, DLocRL does not require hand-crafted features. The experimental results show the effectiveness of DLocRL on fine-grained location recognition and linking with a real-world Twitter dataset. …

NAS-Bench-101
Recent advances in neural architecture search (NAS) demand tremendous computational resources. This makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build NAS-Bench-101, we carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures. We trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset. All together, NAS-Bench-101 contains the metrics of over 5 million models, the largest dataset of its kind thus far. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the pre-computed dataset. We demonstrate its utility by analyzing the dataset as a whole and by benchmarking a range of architecture optimization algorithms. …

Constrained Recurrent Sparse Auto-Encoder (CRsAE)
Convolutional dictionary learning (CDL) has become a popular method for learning sparse representations from data. State-of-the-art algorithms perform dictionary learning (DL) through an optimization-based alternating-minimization procedure that comprises a sparse coding and a dictionary update step respectively. Here, we draw connections between CDL and neural networks by proposing an architecture for CDL termed the constrained recurrent sparse auto-encoder (CRsAE). We leverage the interpretation of the alternating-minimization algorithm for DL as an Expectation-Maximization algorithm to develop auto-encoders (AEs) that, for the first time, enable the simultaneous training of the dictionary and regularization parameter. The forward pass of the encoder, which performs sparse coding, solves the E-step using an encoding matrix and a soft-thresholding non-linearity imposed by the FISTA algorithm. The encoder in this regard is a variant of residual and recurrent neural networks. The M-step is implemented via a two-stage back-propagation. In the first stage, we perform back-propagation through the AE formed by the encoder and a linear decoder whose parameters are tied to the encoder. This stage parallels the dictionary update step in DL. In the second stage, we update the regularization parameter by performing back-propagation through the encoder using a loss function that includes a prior on the parameter motivated by Bayesian statistics. We leverage GPUs to achieve significant computational gains relative to state-of-the-art optimization-based approaches to CDL. We apply CRsAE to spike sorting, the problem of identifying the time of occurrence of neural action potentials in recordings of electrical activity from the brain. We demonstrate on recordings lasting hours that CRsAE speeds up spike sorting by 900x compared to notoriously slow classical algorithms based on convex optimization. …

Participatory Sensing
Participatory sensing is the concept of communities (or other groups of people) contributing sensory information to form a body of knowledge. …