1,630,844 research outputs found
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
© 1979-2012 IEEE. To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract 'Multi-Directional Multi-Level Dual-Cross Patterns' (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme
Landau-Zener-Stuckelberg interference in a multi-anticrossing system
We propose a universal analytical method to study the dynamics of a
multi-anticrossing system subject to driving by one single large-amplitude
triangle pulse, within its time scales smaller than the dephasing time. Our
approach can explain the main features of the Landau-Zener-Stuckelberg
interference patterns recently observed in a tripartite system [Nature
Communications 1:51 (2010)]. In particular, we focus on the effects of the size
of anticrossings on interference and compare the calculated interference
patterns with numerical simulations. In addition, Fourier transform of the
patterns can extract information on the energy level spectrum.Comment: 6 pages, 5 figure
Scale Invariance and Nonlinear Patterns of Human Activity
We investigate if known extrinsic and intrinsic factors fully account for the
complex features observed in recordings of human activity as measured from
forearm motion in subjects undergoing their regular daily routine. We
demonstrate that the apparently random forearm motion possesses previously
unrecognized dynamic patterns characterized by fractal and nonlinear dynamics.
These patterns are unaffected by changes in the average activity level, and
persist when the same subjects undergo time-isolation laboratory experiments
designed to account for the circadian phase and to control the known extrinsic
factors. We attribute these patterns to a novel intrinsic multi-scale dynamic
regulation of human activity.Comment: 4 pages, three figure
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.Comment: Accepted by ICASSP 201
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