11,321 research outputs found
Temporal Attention-Gated Model for Robust Sequence Classification
Typical techniques for sequence classification are designed for
well-segmented sequences which have been edited to remove noisy or irrelevant
parts. Therefore, such methods cannot be easily applied on noisy sequences
expected in real-world applications. In this paper, we present the Temporal
Attention-Gated Model (TAGM) which integrates ideas from attention models and
gated recurrent networks to better deal with noisy or unsegmented sequences.
Specifically, we extend the concept of attention model to measure the relevance
of each observation (time step) of a sequence. We then use a novel gated
recurrent network to learn the hidden representation for the final prediction.
An important advantage of our approach is interpretability since the temporal
attention weights provide a meaningful value for the salience of each time step
in the sequence. We demonstrate the merits of our TAGM approach, both for
prediction accuracy and interpretability, on three different tasks: spoken
digit recognition, text-based sentiment analysis and visual event recognition.Comment: Accepted by CVPR 201
Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC
Machine Learning (ML) techniques are rapidly finding a place among the
methods of High Energy Physics data analysis. Different approaches are explored
concerning how much effort should be put into building high-level variables
based on physics insight into the problem, and when it is enough to rely on
low-level ones, allowing ML methods to find patterns without explicit physics
model.
In this paper we continue the discussion of previous publications on the CP
state of the Higgs boson measurement of the H to tau tau decay channel with the
consecutive tau^pm to rho^pm nu; rho^pm to pi^pm pi^0 and tau^pm to a_1^pm nu;
a_1^pm to rho^0 pi^pm to 3 pi^pm cascade decays. The discrimination of the
Higgs boson CP state is studied as a binary classification problem between
CP-even (scalar) and CP-odd (pseudoscalar), using Deep Neural Network (DNN).
Improvements on the classification from the constraints on directly
non-measurable outgoing neutrinos are discussed. We find, that once added, they
enhance the sensitivity sizably, even if only imperfect information is
provided. In addition to DNN we also evaluate and compare other ML methods:
Boosted Trees (BT), Random Forest (RF) and Support Vector Machine (SVN).Comment: 1+20 pages, 9 figures, 6 tables, extended content and improved
readabilit
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
- …