447 research outputs found
Out-of-Variable Generalization for Discriminative Models
The ability of an agent to do well in new environments is a critical aspect
of intelligence. In machine learning, this ability is known as
or generalization. However,
merely considering differences in data distributions is inadequate for fully
capturing differences between learning environments. In the present paper, we
investigate generalization, which pertains to an
agent's generalization capabilities concerning environments with variables that
were never jointly observed before. This skill closely reflects the process of
animate learning: we, too, explore Nature by probing, observing, and measuring
of variables at any given time. Mathematically,
generalization requires the efficient re-use of past
marginal information, i.e., information over subsets of previously observed
variables. We study this problem, focusing on prediction tasks across
environments that contain overlapping, yet distinct, sets of causes. We show
that after fitting a classifier, the residual distribution in one environment
reveals the partial derivative of the true generating function with respect to
the unobserved causal parent in that environment. We leverage this information
and propose a method that exhibits non-trivial out-of-variable generalization
performance when facing an overlapping, yet distinct, set of causal predictors
Generating entanglement between microwave photons and qubits in multiple cavities coupled by a superconducting qutrit
We discuss how to generate entangled coherent states of four
\textrm{microwave} resonators \textrm{(a.k.a. cavities)} coupled by a
superconducting qubit. We also show \textrm{that} a GHZ state of four
superconducting qubits embedded in four different resonators \textrm{can be
created with this scheme}. In principle, \textrm{the proposed method} can be
extended to create an entangled coherent state of resonators and to prepare
a Greenberger-Horne-Zeilinger (GHZ) state of qubits distributed over
cavities in a quantum network. In addition, it is noted that four resonators
coupled by a coupler qubit may be used as a basic circuit block to build a
two-dimensional quantum network, which is useful for scalable quantum
information processing.Comment: 13 pages, 7 figure
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a large
amount of i.i.d. training data are available for a given task. In a dynamic
environment, though, an intelligent agent needs to be able to transfer
knowledge and re-use learned components across domains. It has been argued that
this may be possible through causal models, aiming to mirror the modularity of
the real world in terms of independent causal mechanisms. However, the true
causal structure underlying a given set of data is generally not identifiable,
so it is desirable to have means to quantify differences between models (e.g.,
between the ground truth and an estimate), on both the observational and
interventional level.
In the present work, we introduce the Interventional Kullback-Leibler (IKL)
divergence to quantify both structural and distributional differences between
models based on a finite set of multi-environment distributions generated by
interventions from the ground truth. Since we generally cannot quantify all
differences between causal models for every finite set of interventional
distributions, we propose a sufficient condition on the intervention targets to
identify subsets of observed variables on which the models provably agree or
disagree
MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes
The multi-modality and stochastic characteristics of human behavior make
motion prediction a highly challenging task, which is critical for autonomous
driving. While deep learning approaches have demonstrated their great potential
in this area, it still remains unsolved to establish a connection between
multiple driving scenes (e.g., merging, roundabout, intersection) and the
design of deep learning models. Current learning-based methods typically use
one unified model to predict trajectories in different scenarios, which may
result in sub-optimal results for one individual scene. To address this issue,
we propose Multi-Scenes Network (aka. MS-Net), which is a multi-path sparse
model trained by an evolutionary process. MS-Net selectively activates a subset
of its parameters during the inference stage to produce prediction results for
each scene. In the training stage, the motion prediction task under
differentiated scenes is abstracted as a multi-task learning problem, an
evolutionary algorithm is designed to encourage the network search of the
optimal parameters for each scene while sharing common knowledge between
different scenes. Our experiment results show that with substantially reduced
parameters, MS-Net outperforms existing state-of-the-art methods on
well-established pedestrian motion prediction datasets, e.g., ETH and UCY, and
ranks the 2nd place on the INTERACTION challenge.Comment: Accepted by IEEE Robotics and Automation Letters (RAL
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