5,118 research outputs found
Confidence Calibration for Systems with Cascaded Predictive Modules
Existing conformal prediction algorithms estimate prediction intervals at
target confidence levels to characterize the performance of a regression model
on new test samples. However, considering an autonomous system consisting of
multiple modules, prediction intervals constructed for individual modules fall
short of accommodating uncertainty propagation over different modules and thus
cannot provide reliable predictions on system behavior. We address this
limitation and present novel solutions based on conformal prediction to provide
prediction intervals calibrated for a predictive system consisting of cascaded
modules (e.g., an upstream feature extraction module and a downstream
regression module). Our key idea is to leverage module-level validation data to
characterize the system-level error distribution without direct access to
end-to-end validation data. We provide theoretical justification and empirical
experimental results to demonstrate the effectiveness of proposed solutions. In
comparison to prediction intervals calibrated for individual modules, our
solutions generate improved intervals with more accurate performance guarantees
for system predictions, which are demonstrated on both synthetic systems and
real-world systems performing overlap prediction for indoor navigation using
the Matterport3D dataset
DiaquaÂbis(N,N′-dibenzylÂethane-1,2-diamine-κ2 N,N′)nickel(II) dichloride N,N-dimethylÂformamide solvate
The asymmetric unit of the title complex, [Ni(C16H20N2)2(H2O)2]Cl2·C3H7NO, consists of two NiII atoms, each lying on an inversion center, two Cl anions, two N,N′-dibenzylÂethane-1,2-diamine ligands, two coordinated water molÂecules and one N,N-dimethylÂformamide solvent molÂecule. Each NiII atom is six-coordinated in a distorted octaÂhedral coordination geometry, with the equatorial plane formed by four N atoms and the axial positions occupied by two water molÂecules. The complex molÂecules are linked into a chain along [001] by N—H⋯Cl, N—H⋯O and O—H⋯Cl hydrogen bonds. The C atoms and H atoms of the solvent molÂecule are disordered over two sites in a ratio of 0.52 (2):0.48 (2)
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Low-resource automatic speech recognition (ASR) is challenging, as the
low-resource target language data cannot well train an ASR model. To solve this
issue, meta-learning formulates ASR for each source language into many small
ASR tasks and meta-learns a model initialization on all tasks from different
source languages to access fast adaptation on unseen target languages. However,
for different source languages, the quantity and difficulty vary greatly
because of their different data scales and diverse phonological systems, which
leads to task-quantity and task-difficulty imbalance issues and thus a failure
of multilingual meta-learning ASR (MML-ASR). In this work, we solve this
problem by developing a novel adversarial meta sampling (AMS) approach to
improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the
task sampling probability for each source language. Specifically, for each
source language, if the query loss is large, it means that its tasks are not
well sampled to train ASR model in terms of its quantity and difficulty and
thus should be sampled more frequently for extra learning. Inspired by this
fact, we feed the historical task query loss of all source language domain into
a network to learn a task sampling policy for adversarially increasing the
current query loss of MML-ASR. Thus, the learnt task sampling policy can master
the learning situation of each language and thus predicts good task sampling
probability for each language for more effective learning. Finally, experiment
results on two multilingual datasets show significant performance improvement
when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS
to other low-resource speech tasks and transfer learning ASR approaches.Comment: accepted in AAAI202
Sexually dimorphic expression of a chicken sex chromosome gene (VCP) reflects differences in gonadal development between males and females
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