1,205 research outputs found
Signature inversion for monotone paths
The aim of this article is to provide a simple sampling procedure to
reconstruct any monotone path from its signature. For every N, we sample a
lattice path of N steps with weights given by the coefficient of the
corresponding word in the signature. We show that these weights on lattice
paths satisfy the large deviations principle. In particular, this implies that
the probability of picking up a "wrong" path is exponentially small in N. The
argument relies on a probabilistic interpretation of the signature for monotone
paths
The insertion method to invert the signature of a path
The signature is a representation of a path as an infinite sequence of its
iterated integrals. Under certain assumptions, the signature characterizes the
path, up to translation and reparameterization. Therefore, a crucial question
of interest is the development of efficient algorithms to invert the signature,
i.e., to reconstruct the path from the information of its (truncated)
signature. In this article, we study the insertion procedure, originally
introduced by Chang and Lyons (2019), from both a theoretical and a practical
point of view. After describing our version of the method, we give its rate of
convergence for piecewise linear paths, accompanied by an implementation in
Pytorch. The algorithm is parallelized, meaning that it is very efficient at
inverting a batch of signatures simultaneously. Its performance is illustrated
with both real-world and simulated examples
Task-Adaptive Negative Class Envision for Few-Shot Open-Set Recognition
Recent works seek to endow recognition systems with the ability to handle the
open world. Few shot learning aims for fast learning of new classes from
limited examples, while open-set recognition considers unknown negative class
from the open world. In this paper, we study the problem of few-shot open-set
recognition (FSOR), which learns a recognition system robust to queries from
new sources with few examples and from unknown open sources. To achieve that,
we mimic human capability of envisioning new concepts from prior knowledge, and
propose a novel task-adaptive negative class envision method (TANE) to model
the open world. Essentially we use an external memory to estimate a negative
class representation. Moreover, we introduce a novel conjugate episode training
strategy that strengthens the learning process. Extensive experiments on four
public benchmarks show that our approach significantly improves the
state-of-the-art performance on few-shot open-set recognition. Besides, we
extend our method to generalized few-shot open-set recognition (GFSOR), where
we also achieve performance gains on MiniImageNet
Decentralized Non-Convex Learning with Linearly Coupled Constraints
Motivated by the need for decentralized learning, this paper aims at
designing a distributed algorithm for solving nonconvex problems with general
linear constraints over a multi-agent network. In the considered problem, each
agent owns some local information and a local variable for jointly minimizing a
cost function, but local variables are coupled by linear constraints. Most of
the existing methods for such problems are only applicable for convex problems
or problems with specific linear constraints. There still lacks a distributed
algorithm for such problems with general linear constraints and under nonconvex
setting. In this paper, to tackle this problem, we propose a new algorithm,
called "proximal dual consensus" (PDC) algorithm, which combines a proximal
technique and a dual consensus method. We build the theoretical convergence
conditions and show that the proposed PDC algorithm can converge to an
-Karush-Kuhn-Tucker solution within
iterations. For computation reduction, the PDC algorithm can choose to perform
cheap gradient descent per iteration while preserving the same order of
iteration complexity. Numerical results are presented
to demonstrate the good performance of the proposed algorithms for solving a
regression problem and a classification problem over a network where agents
have only partial observations of data features
Effective Numerical Simulations of Synchronous Generator System
Synchronous generator system is a complicated dynamical system for energy
transmission, which plays an important role in modern industrial production. In
this article, we propose some predictor-corrector methods and
structure-preserving methods for a generator system based on the first
benchmark model of subsynchronous resonance, among which the
structure-preserving methods preserve a Dirac structure associated with the
so-called port-Hamiltonian descriptor systems. To illustrate this, the
simplified generator system in the form of index-1 differential-algebraic
equations has been derived. Our analyses provide the global error estimates for
a special class of structure-preserving methods called Gauss methods, which
guarantee their superior performance over the PSCAD/EMTDC and the
predictor-corrector methods in terms of computational stability. Numerical
simulations are implemented to verify the effectiveness and advantages of our
methods
Few-Shot Object Detection with Fully Cross-Transformer
Few-shot object detection (FSOD), with the aim to detect novel objects using
very few training examples, has recently attracted great research interest in
the community. Metric-learning based methods have been demonstrated to be
effective for this task using a two-branch based siamese network, and calculate
the similarity between image regions and few-shot examples for detection.
However, in previous works, the interaction between the two branches is only
restricted in the detection head, while leaving the remaining hundreds of
layers for separate feature extraction. Inspired by the recent work on vision
transformers and vision-language transformers, we propose a novel Fully
Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer
into both the feature backbone and detection head. The asymmetric-batched
cross-attention is proposed to aggregate the key information from the two
branches with different batch sizes. Our model can improve the few-shot
similarity learning between the two branches by introducing the multi-level
interactions. Comprehensive experiments on both PASCAL VOC and MSCOCO FSOD
benchmarks demonstrate the effectiveness of our model.Comment: Accepted by CVPR 202
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