810 research outputs found
Single Shot Temporal Action Detection
Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-art methods adopt the "detection by classification" framework:
first do proposal, and then classify proposals. The main drawback of this
framework is that the boundaries of action instance proposals have been fixed
during the classification step. To address this issue, we propose a novel
Single Shot Action Detector (SSAD) network based on 1D temporal convolutional
layers to skip the proposal generation step via directly detecting action
instances in untrimmed video. On pursuit of designing a particular SSAD network
that can work effectively for temporal action detection, we empirically search
for the best network architecture of SSAD due to lacking existing models that
can be directly adopted. Moreover, we investigate into input feature types and
fusion strategies to further improve detection accuracy. We conduct extensive
experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When
setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD
significantly outperforms other state-of-the-art systems by increasing mAP from
19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201
First principles calculation of vibrational Raman spectra in large systems: signature of small rings in crystalline SiO2
We present an approach for the efficient calculation of vibrational Raman
intensities in periodic systems within density functional theory. The Raman
intensities are computed from the second order derivative of the electronic
density matrix with respect to a uniform electric field. In contrast to
previous approaches, the computational effort required by our method for the
evaluation of the intensities is negligible compared to that required for the
calculation of vibrational frequencies. As a first application, we study the
signature of 3- and 4-membered rings in the the Raman spectra of several
polymorphs of SiO2, including a zeolite having 102 atoms per unit cell.Comment: 4 pages, 2 figures, revtex4 Minor corrections; accepted in Phys. Rev.
Let
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interactions, enabling to model nonlinear potential-energy
surfaces or enhancing the exploration of chemical compound space. Here we
present the deep learning architecture SchNet that is specifically designed to
model atomistic systems by making use of continuous-filter convolutional
layers. We demonstrate the capabilities of SchNet by accurately predicting a
range of properties across chemical space for \emph{molecules and materials}
where our model learns chemically plausible embeddings of atom types across the
periodic table. Finally, we employ SchNet to predict potential-energy surfaces
and energy-conserving force fields for molecular dynamics simulations of small
molecules and perform an exemplary study of the quantum-mechanical properties
of C-fullerene that would have been infeasible with regular ab initio
molecular dynamics
Ab initio studies of phonon softening and high pressure phase transitions of alpha-quartz SiO2
Density functional perturbation theory calculations of alpha-quartz using
extended norm conserving pseudopotentials have been used to study the elastic
properties and phonon dispersion relations along various high symmetry
directions as a function of bulk, uniaxial and non-hydrostatic pressure. The
computed equation of state, elastic constants and phonon frequencies are found
to be in good agreement with available experimental data. A zone boundary (1/3,
1/3, 0) K-point phonon mode becomes soft for pressures above P=32 GPa. Around
the same pressure, studies of the Born stability criteria reveal that the
structure is mechanically unstable. The phonon and elastic softening are
related to the high pressure phase transitions and amorphization of quartz and
these studies suggest that the mean transition pressure is lowered under
non-hydrostatic conditions. Application of uniaxial pressure, results in a
post-quartz crystalline monoclinic C2 structural transition in the vicinity of
the K-point instability. This structure, intermediate between quartz and
stishovite has two-thirds of the silicon atoms in octahedral coordination while
the remaining silicon atoms remain tetrahedrally coordinated. This novel
monoclinic C2 polymorph of silica, which is found to be metastable under
ambient conditions, is possibly one of the several competing dense forms of
silica containing octahedrally coordinated silicon. The possible role of high
pressure ferroelastic phases in causing pressure induced amorphization in
silica are discussed.Comment: 17 pages, 8 figs., 8 Table
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
Anharmonic effects in the A15 compounds induced by sublattice distortions
We demonstrate that elastic anomalies and lattice instabilities in the the
A15 compounds are describable in terms of first-principles LDA electronic
structure calculations. We show that at T=0 V_3Si, V_3Ge, and Nb_3Sn are
intrinsically unstable against shears with elastic moduli C_11-C_12 and C_44,
and that the zone center phonons, Gamma_2 and Gamma_12, are either unstable or
extremely soft. We demonstrate that sublattice relaxation (internal strain)
effects are key to understanding the behavior of the A15 materials.Comment: 5 pages, RevTex, 3 postscript figures, Submitted to Phys. Rev. Lett.
Apr. 23, 1997 July 7, 1997: minor corrections, final accepted versio
Geometric deep learning
The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas
On Sampling Strategies for Neural Network-based Collaborative Filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.Comment: This is a longer version (with supplementary attached) of the KDD'17
pape
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
We explore the potential of feed-forward deep neural networks (DNNs) for
emulating cloud superparameterization in realistic geography, using offline
fits to data from the Super Parameterized Community Atmospheric Model. To
identify the network architecture of greatest skill, we formally optimize
hyperparameters using ~250 trials. Our DNN explains over 70 percent of the
temporal variance at the 15-minute sampling scale throughout the mid-to-upper
troposphere. Autocorrelation timescale analysis compared against DNN skill
suggests the less good fit in the tropical, marine boundary layer is driven by
neural network difficulty emulating fast, stochastic signals in convection.
However, spectral analysis in the temporal domain indicates skillful emulation
of signals on diurnal to synoptic scales. A close look at the diurnal cycle
reveals correct emulation of land-sea contrasts and vertical structure in the
heating and moistening fields, but some distortion of precipitation.
Sensitivity tests targeting precipitation skill reveal complementary effects of
adding positive constraints vs. hyperparameter tuning, motivating the use of
both in the future. A first attempt to force an offline land model with DNN
emulated atmospheric fields produces reassuring results further supporting
neural network emulation viability in real-geography settings. Overall, the fit
skill is competitive with recent attempts by sophisticated Residual and
Convolutional Neural Network architectures trained on added information,
including memory of past states. Our results confirm the parameterizability of
superparameterized convection with continents through machine learning and we
highlight advantages of casting this problem locally in space and time for
accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of
Advances in Modeling Earth Systems April 202
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