237 research outputs found
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
Reinforcement learning in partially observed Markov decision processes
(POMDPs) faces two challenges. (i) It often takes the full history to predict
the future, which induces a sample complexity that scales exponentially with
the horizon. (ii) The observation and state spaces are often continuous, which
induces a sample complexity that scales exponentially with the extrinsic
dimension. Addressing such challenges requires learning a minimal but
sufficient representation of the observation and state histories by exploiting
the structure of the POMDP.
To this end, we propose a reinforcement learning algorithm named Embed to
Control (ETC), which learns the representation at two levels while optimizing
the policy.~(i) For each step, ETC learns to represent the state with a
low-dimensional feature, which factorizes the transition kernel. (ii) Across
multiple steps, ETC learns to represent the full history with a low-dimensional
embedding, which assembles the per-step feature. We integrate (i) and (ii) in a
unified framework that allows a variety of estimators (including maximum
likelihood estimators and generative adversarial networks). For a class of
POMDPs with a low-rank structure in the transition kernel, ETC attains an
sample complexity that scales polynomially with the horizon
and the intrinsic dimension (that is, the rank). Here is the
optimality gap. To our best knowledge, ETC is the first sample-efficient
algorithm that bridges representation learning and policy optimization in
POMDPs with infinite observation and state spaces.Comment: Accepted by ICLR 202
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained
widespread attention, aiming to super sample medical volumes at arbitrary
scales via a single model. However, existing MIASSR methods face two major
limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited
generalization ability, which restricts their application in various scenarios.
To overcome these limitations, we propose Cube-based Neural Radiance Field
(CuNeRF), a zero-shot MIASSR framework that can yield medical images at
arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR
methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF
focuses on building a coordinate-intensity continuous representation from LR
volumes without the need for HR references. This is achieved by the proposed
differentiable modules: including cube-based sampling, isotropic volume
rendering, and cube-based hierarchical rendering. Through extensive experiments
on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we
demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF
yields better visual verisimilitude and reduces aliasing artifacts at various
upsampling factors. Moreover, our CuNeRF does not need any LR-HR training
pairs, which is more flexible and easier to be used than others. Our code will
be publicly available soon
Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task
Characterizing users' interests accurately plays a significant role in an
effective recommender system. The sequential recommender system can learn
powerful hidden representations of users from successive user-item interactions
and dynamic users' preferences. To analyze such sequential data, conventional
methods mainly include Markov Chains (MCs) and Recurrent Neural Networks
(RNNs). Recently, the use of self-attention mechanisms and bi-directional
architectures have gained much attention. However, there still exists a major
limitation in previous works that they only model the user's main purposes in
the behavioral sequences separately and locally, and they lack the global
representation of the user's whole sequential behavior. To address this
limitation, we propose a novel bidirectional sequential recommendation
algorithm that integrates the user's local purposes with the global preference
by additive supervision of the matching task. We combine the mask task with the
matching task in the training process of the bidirectional encoder. A new
sample production method is also introduced to alleviate the effect of mask
noise. Our proposed model can not only learn bidirectional semantics from
users' behavioral sequences but also explicitly produces user representations
to capture user's global preference. Extensive empirical studies demonstrate
our approach considerably outperforms various state-of-the-art models.Comment: Accepted by ICONIP202
Hard Nominal Example-aware Template Mutual Matching for Industrial Anomaly Detection
Anomaly detectors are widely used in industrial production to detect and
localize unknown defects in query images. These detectors are trained on
nominal images and have shown success in distinguishing anomalies from most
normal samples. However, hard-nominal examples are scattered and far apart from
most normalities, they are often mistaken for anomalies by existing anomaly
detectors. To address this problem, we propose a simple yet efficient method:
\textbf{H}ard Nominal \textbf{E}xample-aware \textbf{T}emplate \textbf{M}utual
\textbf{M}atching (HETMM). Specifically, \textit{HETMM} aims to construct a
robust prototype-based decision boundary, which can precisely distinguish
between hard-nominal examples and anomalies, yielding fewer false-positive and
missed-detection rates. Moreover, \textit{HETMM} mutually explores the
anomalies in two directions between queries and the template set, and thus it
is capable to capture the logical anomalies. This is a significant advantage
over most anomaly detectors that frequently fail to detect logical anomalies.
Additionally, to meet the speed-accuracy demands, we further propose
\textbf{P}ixel-level \textbf{T}emplate \textbf{S}election (PTS) to streamline
the original template set. \textit{PTS} selects cluster centres and
hard-nominal examples to form a tiny set, maintaining the original decision
boundaries. Comprehensive experiments on five real-world datasets demonstrate
that our methods yield outperformance than existing advances under the
real-time inference speed. Furthermore, \textit{HETMM} can be hot-updated by
inserting novel samples, which may promptly address some incremental learning
issues
SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks
Spiking neural networks are efficient computation models for low-power
environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions
are successful techniques for SNN training. Nevertheless, the spike-base BP
training is slow and requires large memory costs. Though ANN2NN provides a
low-cost way to train SNNs, it requires many inference steps to mimic the
well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN
(SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The
SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN
and SNN and b) spiking mapping units. Firstly, the architecture trains the
weight-sharing parameters on the ANN branch, resulting in fast training and low
memory costs for SNN. Secondly, the spiking mapping units ensure that the
activation values of the ANN are the spiking features. As a result, the
classification error of the SNN can be optimized by training the ANN branch.
Besides, we design an adaptive threshold adjustment (ATA) algorithm to address
the noisy spike problem. Experiment results show that our SNN2ANN-based models
perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet).
Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps,
0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the
Spike-based BP model
Observation of the chiral anomaly induced negative magneto-resistance in 3D Weyl semi-metal TaAs
Weyl semi-metal is the three dimensional analog of graphene. According to the
quantum field theory, the appearance of Weyl points near the Fermi level will
cause novel transport phenomena related to chiral anomaly. In the present
paper, we report the first experimental evidence for the long-anticipated
negative magneto-resistance generated by the chiral anomaly in a newly
predicted time-reversal invariant Weyl semi-metal material TaAs. Clear
Shubnikov de Haas oscillations (SdH) have been detected starting from very weak
magnetic field. Analysis of the SdH peaks gives the Berry phase accumulated
along the cyclotron orbits to be {\pi}, indicating the existence of Weyl
points.Comment: Submitted in February'1
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