664 research outputs found
Prefix-Tuning Based Unsupervised Text Style Transfer
Unsupervised text style transfer aims at training a generative model that can
alter the style of the input sentence while preserving its content without
using any parallel data. In this paper, we employ powerful pre-trained large
language models and present a new prefix-tuning-based method for unsupervised
text style transfer. We construct three different kinds of prefixes, i.e.,
\textit{shared prefix, style prefix}, and \textit{content prefix}, to encode
task-specific information, target style, and the content information of the
input sentence, respectively. Compared to embeddings used by previous works,
the proposed prefixes can provide richer information for the model.
Furthermore, we adopt a recursive way of using language models in the process
of style transfer. This strategy provides a more effective way for the
interactions between the input sentence and GPT-2, helps the model construct
more informative prefixes, and thus, helps improve the performance. Evaluations
on the well-known datasets show that our method outperforms the
state-of-the-art baselines. Results, analysis of ablation studies, and
subjective evaluations from humans are also provided for a deeper understanding
of the proposed method
Causal Reinforcement Learning: A Survey
Reinforcement learning is an essential paradigm for solving sequential
decision problems under uncertainty. Despite many remarkable achievements in
recent decades, applying reinforcement learning methods in the real world
remains challenging. One of the main obstacles is that reinforcement learning
agents lack a fundamental understanding of the world and must therefore learn
from scratch through numerous trial-and-error interactions. They may also face
challenges in providing explanations for their decisions and generalizing the
acquired knowledge. Causality, however, offers a notable advantage as it can
formalize knowledge in a systematic manner and leverage invariance for
effective knowledge transfer. This has led to the emergence of causal
reinforcement learning, a subfield of reinforcement learning that seeks to
enhance existing algorithms by incorporating causal relationships into the
learning process. In this survey, we comprehensively review the literature on
causal reinforcement learning. We first introduce the basic concepts of
causality and reinforcement learning, and then explain how causality can
address core challenges in non-causal reinforcement learning. We categorize and
systematically review existing causal reinforcement learning approaches based
on their target problems and methodologies. Finally, we outline open issues and
future directions in this emerging field.Comment: 48 pages, 10 figure
Smooth and Stepwise Self-Distillation for Object Detection
Distilling the structured information captured in feature maps has
contributed to improved results for object detection tasks, but requires
careful selection of baseline architectures and substantial pre-training.
Self-distillation addresses these limitations and has recently achieved
state-of-the-art performance for object detection despite making several
simplifying architectural assumptions. Building on this work, we propose Smooth
and Stepwise Self-Distillation (SSSD) for object detection. Our SSSD
architecture forms an implicit teacher from object labels and a feature pyramid
network backbone to distill label-annotated feature maps using Jensen-Shannon
distance, which is smoother than distillation losses used in prior work. We
additionally add a distillation coefficient that is adaptively configured based
on the learning rate. We extensively benchmark SSSD against a baseline and two
state-of-the-art object detector architectures on the COCO dataset by varying
the coefficients and backbone and detector networks. We demonstrate that SSSD
achieves higher average precision in most experimental settings, is robust to a
wide range of coefficients, and benefits from our stepwise distillation
procedure.Comment: Accepted by International Conference on Image Processing (ICIP) 202
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Most of ranking models are trained only with displayed items (most are hot
items), but they are utilized to retrieve items in the entire space which
consists of both displayed and non-displayed items (most are long-tail items).
Due to the sample selection bias, the long-tail items lack sufficient records
to learn good feature representations, i.e. data sparsity and cold start
problems. The resultant distribution discrepancy between displayed and
non-displayed items would cause poor long-tail performance. To this end, we
propose an entire space adaptation model (ESAM) to address this problem from
the perspective of domain adaptation (DA). ESAM regards displayed and
non-displayed items as source and target domains respectively. Specifically, we
design the attribute correlation alignment that considers the correlation
between high-level attributes of the item to achieve distribution alignment.
Furthermore, we introduce two effective regularization strategies, i.e.
\textit{center-wise clustering} and \textit{self-training} to improve DA
process. Without requiring any auxiliary information and auxiliary domains,
ESAM transfers the knowledge from displayed items to non-displayed items for
alleviating the distribution inconsistency. Experiments on two public datasets
and a large-scale industrial dataset collected from Taobao demonstrate that
ESAM achieves state-of-the-art performance, especially in the long-tail space.
Besides, we deploy ESAM to the Taobao search engine, leading to significant
improvement on online performance. The code is available at
\url{https://github.com/A-bone1/ESAM.git}Comment: Accept by SIGIR-202
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Agitation is one of the most prevalent symptoms in people with dementia (PwD)
that can place themselves and the caregiver's safety at risk. Developing
objective agitation detection approaches is important to support health and
safety of PwD living in a residential setting. In a previous study, we
collected multimodal wearable sensor data from 17 participants for 600 days and
developed machine learning models for predicting agitation in one-minute
windows. However, there are significant limitations in the dataset, such as
imbalance problem and potential imprecise labels as the occurrence of agitation
is much rarer in comparison to the normal behaviours. In this paper, we first
implement different undersampling methods to eliminate the imbalance problem,
and come to the conclusion that only 20\% of normal behaviour data are adequate
to train a competitive agitation detection model. Then, we design a weighted
undersampling method to evaluate the manual labeling mechanism given the
ambiguous time interval (ATI) assumption. After that, the postprocessing method
of cumulative class re-decision (CCR) is proposed based on the historical
sequential information and continuity characteristic of agitation, improving
the decision-making performance for the potential application of agitation
detection system. The results show that a combination of undersampling and CCR
improves F1-score and other metrics to varying degrees with less training time
and data used, and inspires a way to find the potential range of optimal
threshold reference for clinical purpose.Comment: 19 pages, 8 figure
Acupuncture Treatment for Post-Stroke Depression: Intestinal Microbiota and Its Role
Stroke-induced depression is a common complication and an important risk factor for disability. Besides psychiatric symptoms, depressed patients may also exhibit a variety of gastrointestinal symptoms, and even take gastrointestinal symptoms as the primary reason for medical treatment. It is well documented that stress may disrupt the balance of the gut microbiome in patients suffering from post-stroke depression (PSD), and that disruption of the gut microbiome is closely related to the severity of the condition in depressed patients. Therefore, maintaining the balance of intestinal microbiota can be the focus of research on the mechanism of acupuncture in the treatment of PSD. Furthermore, stroke can be effectively treated with acupuncture at all stages and it may act as a special microecological regulator by regulating intestinal microbiota as well. In this article, we reviewed the studies on changing intestinal microbiota after acupuncture treatment and examined the existing problems and development prospects of acupuncture, microbiome, and poststroke depression, in order to provide new ideas for future acupuncture research
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