41 research outputs found
Approximating Word Ranking and Negative Sampling for Word Embedding
CBOW (Continuous Bag-Of-Words) is one of the most commonly used techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling distribution of negative words. To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding. Specifically, we first formalize word embedding as a ranking problem. Then, we weigh the positive words by their ranks such that highly ranked words have more importance, and adopt a dynamic sampling strategy to select informative negative words. In addition, an approximation method is designed to efficiently compute word ranks. Empirical experiments show that OptRank consistently outperforms its counterparts on a benchmark dataset with different sampling scales, especially when the sampled subset is small. The code and datasets can be obtained from https://github.com/ouououououou/OptRank
Boundary integrated neural networks (BINNs) for acoustic radiation and scattering
This paper presents a novel approach called the boundary integrated neural
networks (BINNs) for analyzing acoustic radiation and scattering. The method
introduces fundamental solutions of the time-harmonic wave equation to encode
the boundary integral equations (BIEs) within the neural networks, replacing
the conventional use of the governing equation in physics-informed neural
networks (PINNs). This approach offers several advantages. Firstly, the input
data for the neural networks in the BINNs only require the coordinates of
"boundary" collocation points, making it highly suitable for analyzing acoustic
fields in unbounded domains. Secondly, the loss function of the BINNs is not a
composite form, and has a fast convergence. Thirdly, the BINNs achieve
comparable precision to the PINNs using fewer collocation points and hidden
layers/neurons. Finally, the semi-analytic characteristic of the BIEs
contributes to the higher precision of the BINNs. Numerical examples are
presented to demonstrate the performance of the proposed method
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Jointing visual-semantic embeddings (VSE) have become a research hotpot for
the task of image annotation, which suffers from the issue of semantic gap,
i.e., the gap between images' visual features (low-level) and labels' semantic
features (high-level). This issue will be even more challenging if visual
features cannot be retrieved from images, that is, when images are only denoted
by numerical IDs as given in some real datasets. The typical way of existing
VSE methods is to perform a uniform sampling method for negative examples that
violate the ranking order against positive examples, which requires a
time-consuming search in the whole label space. In this paper, we propose a
fast adaptive negative sampler that can work well in the settings of no figure
pixels available. Our sampling strategy is to choose the negative examples that
are most likely to meet the requirements of violation according to the latent
factors of images. In this way, our approach can linearly scale up to large
datasets. The experiments demonstrate that our approach converges 5.02x faster
than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x
on NUS-WIDE datasets, as well as better ranking accuracy across datasets.Comment: Published by The Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Jointing visual-semantic embeddings (VSE) have become a research hotpot for
the task of image annotation, which suffers from the issue of semantic gap,
i.e., the gap between images' visual features (low-level) and labels' semantic
features (high-level). This issue will be even more challenging if visual
features cannot be retrieved from images, that is, when images are only denoted
by numerical IDs as given in some real datasets. The typical way of existing
VSE methods is to perform a uniform sampling method for negative examples that
violate the ranking order against positive examples, which requires a
time-consuming search in the whole label space. In this paper, we propose a
fast adaptive negative sampler that can work well in the settings of no figure
pixels available. Our sampling strategy is to choose the negative examples that
are most likely to meet the requirements of violation according to the latent
factors of images. In this way, our approach can linearly scale up to large
datasets. The experiments demonstrate that our approach converges 5.02x faster
than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x
on NUS-WIDE datasets, as well as better ranking accuracy across datasets.Comment: Published by The Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Adapters, a plug-in neural network module with some tunable parameters, have
emerged as a parameter-efficient transfer learning technique for adapting
pre-trained models to downstream tasks, especially for natural language
processing (NLP) and computer vision (CV) fields. Meanwhile, learning
recommendation models directly from raw item modality features -- e.g., texts
of NLP and images of CV -- can enable effective and transferable recommender
systems (called TransRec). In view of this, a natural question arises: can
adapter-based learning techniques achieve parameter-efficient TransRec with
good performance?
To this end, we perform empirical studies to address several key
sub-questions. First, we ask whether the adapter-based TransRec performs
comparably to TransRec based on standard full-parameter fine-tuning? does it
hold for recommendation with different item modalities, e.g., textual RS and
visual RS. If yes, we benchmark these existing adapters, which have been shown
to be effective in NLP and CV tasks, in the item recommendation settings.
Third, we carefully study several key factors for the adapter-based TransRec in
terms of where and how to insert these adapters? Finally, we look at the
effects of adapter-based TransRec by either scaling up its source training data
or scaling down its target training data. Our paper provides key insights and
practical guidance on unified & transferable recommendation -- a less studied
recommendation scenario. We promise to release all code & datasets for future
research
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Learning large-scale pre-trained models on broad-ranging data and then
transfer to a wide range of target tasks has become the de facto paradigm in
many machine learning (ML) communities. Such big models are not only strong
performers in practice but also offer a promising way to break out of the
task-specific modeling restrictions, thereby enabling task-agnostic and unified
ML systems. However, such a popular paradigm is mainly unexplored by the
recommender systems (RS) community. A critical issue is that standard
recommendation models are primarily built on categorical identity features.
That is, the users and the interacted items are represented by their unique
IDs, which are generally not shareable across different systems or platforms.
To pursue the transferable recommendations, we propose studying pre-trained RS
models in a novel scenario where a user's interaction feedback involves a
mixture-of-modality (MoM) items, e.g., text and images. We then present
TransRec, a very simple modification made on the popular ID-based RS framework.
TransRec learns directly from the raw features of the MoM items in an
end-to-end training manner and thus enables effective transfer learning under
various scenarios without relying on overlapped users or items. We empirically
study the transferring ability of TransRec across four different real-world
recommendation settings. Besides, we look at its effects by scaling source and
target data size. Our results suggest that learning neural recommendation
models from MoM feedback provides a promising way to realize universal RS
Non-suicidal self-injury and suicidal ideation among adolescents: the chain-mediating role of rumination and decentering
ObjectiveTo explore the relationship between non-suicidal self-injury and suicidal ideation in adolescents and examine the roles of rumination and decentering in that relationship.MethodBy means of a questionnaire, 175 adolescent patients in a psychiatric hospital in Fujian Province were given the Functional Assessment of Self-Mutilation: Chinese Version, Positive and Negative Suicide Ideation, Ruminative Response Scale: Chinese Version, and Experiences Questionnaire: Decentering Scale.Results(1) Adolescent non-suicidal self-injury was significantly positively related to suicidal ideation and rumination and significantly negatively related to decentering. Suicidal ideation was significantly positively related to rumination and significantly negatively related to decentering. Rumination was significantly negatively related to decentering. (2) Rumination and decentering played a complete chain-mediating role between non-suicidal self-injury and suicidal ideation. Non-suicidal self-injury was found to indirectly affect suicidal ideation along three pathways: the independent mediating role of rumination (the mediating effect accounted for 40.166%), independent mediating role of decentering (the mediating effect accounted for 41.274%), and chain-mediating role of rumination and decentering (the mediating effect accounted for 14.958%).ConclusionAdolescent non-suicidal self-injury can indirectly affect suicidal ideation through rumination and decentering. In the future, mindfulness and other methods should be used to improve individuals’ levels of decentering and cultivate emotional regulation abilities, so as to reduce the incidence of non-suicidal self-injury and suicide in adolescents
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Learning a recommender system model from an item's raw modality features
(such as image, text, audio, etc.), called MoRec, has attracted growing
interest recently. One key advantage of MoRec is that it can easily benefit
from advances in other fields, such as natural language processing (NLP) and
computer vision (CV). Moreover, it naturally supports transfer learning across
different systems through modality features, known as transferable recommender
systems, or TransRec.
However, so far, TransRec has made little progress, compared to
groundbreaking foundation models in the fields of NLP and CV. The lack of
large-scale, high-quality recommendation datasets poses a major obstacle. To
this end, we introduce NineRec, a TransRec dataset suite that includes a
large-scale source domain recommendation dataset and nine diverse target domain
recommendation datasets. Each item in NineRec is represented by a text
description and a high-resolution cover image. With NineRec, we can implement
TransRec models in an end-to-end training manner instead of using pre-extracted
invariant features. We conduct a benchmark study and empirical analysis of
TransRec using NineRec, and our findings provide several valuable insights. To
support further research, we make our code, datasets, benchmarks, and
leaderboards publicly available at https://github.com/westlake-repl/NineRec
Extended Wiener-Khinchin theorem for quantum spectral analysis
The classical Wiener-Khinchin theorem (WKT), which can extract spectral
information by classical interferometers through Fourier transform, is a
fundamental theorem used in many disciplines. However, there is still need for
a quantum version of WKT, which could connect correlated biphoton spectral
information by quantum interferometers. Here, we extend the classical WKT to
its quantum counterpart, i.e., extended WKT (e-WKT), which is based on
two-photon quantum interferometry. According to the e-WKT, the
difference-frequency distribution of the biphoton wavefunctions can be
extracted by applying a Fourier transform on the time-domain Hong-Ou-Mandel
interference (HOMI) patterns, while the sum-frequency distribution can be
extracted by applying a Fourier transform on the time-domain NOON state
interference (NOONI) patterns. We also experimentally verified the WKT and
e-WKT in a Mach-Zehnder interference (MZI), a HOMI and a NOONI. This theorem
can be directly applied to quantum spectroscopy, where the spectral correlation
information of biphotons can be obtained from time-domain quantum interferences
by Fourier transform. This may open a new pathway for the study of light-matter
interaction at the single photon level.Comment: 13 pages, 5 figure