5,575 research outputs found
Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes
Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns
Revisiting the Hierarchical Multiscale LSTM
Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art
language model that learns interpretable structure from character-level input.
Such models can provide fertile ground for (cognitive) computational
linguistics studies. However, the high complexity of the architecture, training
procedure and implementations might hinder its applicability. We provide a
detailed reproduction and ablation study of the architecture, shedding light on
some of the potential caveats of re-purposing complex deep-learning
architectures. We further show that simplifying certain aspects of the
architecture can in fact improve its performance. We also investigate the
linguistic units (segments) learned by various levels of the model, and argue
that their quality does not correlate with the overall performance of the model
on language modeling.Comment: To appear in COLING 2018 (reproduction track
Recurrent Convolutional Neural Networks for Scene Parsing
Scene parsing is a technique that consist on giving a label to all pixels in
an image according to the class they belong to. To ensure a good visual
coherence and a high class accuracy, it is essential for a scene parser to
capture image long range dependencies. In a feed-forward architecture, this can
be simply achieved by considering a sufficiently large input context patch,
around each pixel to be labeled. We propose an approach consisting of a
recurrent convolutional neural network which allows us to consider a large
input context, while limiting the capacity of the model. Contrary to most
standard approaches, our method does not rely on any segmentation methods, nor
any task-specific features. The system is trained in an end-to-end manner over
raw pixels, and models complex spatial dependencies with low inference cost. As
the context size increases with the built-in recurrence, the system identifies
and corrects its own errors. Our approach yields state-of-the-art performance
on both the Stanford Background Dataset and the SIFT Flow Dataset, while
remaining very fast at test time
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Predicting the number of clock cycles a processor takes to execute a block of
assembly instructions in steady state (the throughput) is important for both
compiler designers and performance engineers. Building an analytical model to
do so is especially complicated in modern x86-64 Complex Instruction Set
Computer (CISC) machines with sophisticated processor microarchitectures in
that it is tedious, error prone, and must be performed from scratch for each
processor generation. In this paper we present Ithemal, the first tool which
learns to predict the throughput of a set of instructions. Ithemal uses a
hierarchical LSTM--based approach to predict throughput based on the opcodes
and operands of instructions in a basic block. We show that Ithemal is more
accurate than state-of-the-art hand-written tools currently used in compiler
backends and static machine code analyzers. In particular, our model has less
than half the error of state-of-the-art analytical models (LLVM's llvm-mca and
Intel's IACA). Ithemal is also able to predict these throughput values just as
fast as the aforementioned tools, and is easily ported across a variety of
processor microarchitectures with minimal developer effort.Comment: Published at 36th International Conference on Machine Learning (ICML)
201
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
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