192 research outputs found
Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware Generative Adversarial Network
3D-aware Generative Adversarial Networks (3D-GANs) currently exhibit
artifacts in their 3D geometrical modeling, such as mesh imperfections and
holes. These shortcomings are primarily attributed to the limited availability
of annotated 3D data, leading to a constrained "valid latent area" for
satisfactory modeling. To address this, we present a Self-Supervised Learning
(SSL) technique tailored as an auxiliary loss for any 3D-GAN, designed to
improve its 3D geometrical modeling capabilities. Our approach pioneers an
inversion technique for 3D-GANs, integrating an encoder that performs adaptive
spatially-varying range operations. Utilizing this inversion, we introduce the
Cyclic Generative Constraint (CGC), aiming to densify the valid latent space.
The CGC operates via augmented local latent vectors that maintain the same
geometric form, and it imposes constraints on the cycle path outputs,
specifically the generator-encoder-generator sequence. This SSL methodology
seamlessly integrates with the inherent GAN loss, ensuring the integrity of
pre-existing 3D-GAN architectures without necessitating alterations. We
validate our approach with comprehensive experiments across various datasets
and architectures, underscoring its efficacy. Our project website:
https://3dgan-ssl.github.ioComment: 13 pages, 12 figures, 6 table
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning
Recent knowledge enhanced pre-trained language models have shown remarkable
performance on downstream tasks by incorporating structured knowledge from
external sources into language models. However, they usually suffer from a
heterogeneous information alignment problem and a noisy knowledge injection
problem. For complex reasoning, the contexts contain rich knowledge that
typically exists in complex and sparse forms. In order to model structured
knowledge in the context and avoid these two problems, we propose to unify
structure reasoning and language model pre-training. It identifies four types
of elementary knowledge structures from contexts to construct structured
queries, and utilizes the box embedding method to conduct explicit structure
reasoning along queries during language modeling. To fuse textual and
structured semantics, we utilize contextual language representations of
knowledge structures to initialize their box embeddings for structure
reasoning. We conduct experiments on complex language reasoning and knowledge
graph (KG) reasoning tasks. The results show that our model can effectively
enhance the performance of complex reasoning of both language and KG
modalities.Comment: 10 pages, 4 figures, 6 table
Cross-Lingual Knowledge Editing in Large Language Models
Knowledge editing aims to change language models' performance on several
special cases (i.e., editing scope) by infusing the corresponding expected
knowledge into them. With the recent advancements in large language models
(LLMs), knowledge editing has been shown as a promising technique to adapt LLMs
to new knowledge without retraining from scratch. However, most of the previous
studies neglect the multi-lingual nature of some main-stream LLMs (e.g., LLaMA,
ChatGPT and GPT-4), and typically focus on monolingual scenarios, where LLMs
are edited and evaluated in the same language. As a result, it is still unknown
the effect of source language editing on a different target language. In this
paper, we aim to figure out this cross-lingual effect in knowledge editing.
Specifically, we first collect a large-scale cross-lingual synthetic dataset by
translating ZsRE from English to Chinese. Then, we conduct English editing on
various knowledge editing methods covering different paradigms, and evaluate
their performance in Chinese, and vice versa. To give deeper analyses of the
cross-lingual effect, the evaluation includes four aspects, i.e., reliability,
generality, locality and portability. Furthermore, we analyze the inconsistent
behaviors of the edited models and discuss their specific challenges
DropMessage: Unifying Random Dropping for Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools for graph representation
learning. Despite their rapid development, GNNs also faces some challenges,
such as over-fitting, over-smoothing, and non-robustness. Previous works
indicate that these problems can be alleviated by random dropping methods,
which integrate noises into models by randomly masking parts of the input.
However, some open-ended problems of random dropping on GNNs remain to solve.
First, it is challenging to find a universal method that are suitable for all
cases considering the divergence of different datasets and models. Second,
random noises introduced to GNNs cause the incomplete coverage of parameters
and unstable training process. In this paper, we propose a novel random
dropping method called DropMessage, which performs dropping operations directly
on the message matrix and can be applied to any message-passing GNNs.
Furthermore, we elaborate the superiority of DropMessage: it stabilizes the
training process by reducing sample variance; it keeps information diversity
from the perspective of information theory, which makes it a theoretical upper
bound of other methods. Also, we unify existing random dropping methods into
our framework and analyze their effects on GNNs. To evaluate our proposed
method, we conduct experiments that aims for multiple tasks on five public
datasets and two industrial datasets with various backbone models. The
experimental results show that DropMessage has both advantages of effectiveness
and generalization
C-Silicon-based metasurfaces for aperture-robust spectrometer/imaging with angle integration
Compared with conventional grating-based spectrometers, reconstructive
spectrometers based on spectrally engineered filtering have the advantage of
miniaturization because of the less demand for dispersive optics and free
propagation space. However, available reconstructive spectrometers fail to
balance the performance on operational bandwidth, spectral diversity and
angular stability. In this work, we proposed a compact silicon metasurfaces
based spectrometer/camera. After angle integration, the spectral response of
the system is robust to angle/aperture within a wide working bandwidth from
400nm to 800nm. It is experimentally demonstrated that the proposed method
could maintain the spectral consistency from F/1.8 to F/4 (The corresponding
angle of incident light ranges from 7{\deg} to 16{\deg}) and the incident
hyperspectral signal could be accurately reconstructed with a fidelity
exceeding 99%. Additionally, a spectral imaging system with 400x400 pixels is
also established in this work. The accurate reconstructed hyperspectral image
indicates that the proposed aperture-robust spectrometer has the potential to
be extended as a high-resolution broadband hyperspectral camera
Programming by Example Made Easy
Programming by example (PBE) is an emerging programming paradigm that
automatically synthesizes programs specified by user-provided input-output
examples. Despite the convenience for end-users, implementing PBE tools often
requires strong expertise in programming language and synthesis algorithms.
Such a level of knowledge is uncommon among software developers. It greatly
limits the broad adoption of PBE by the industry. To facilitate the adoption of
PBE techniques, we propose a PBE framework called Bee, which leverages an
"entity-action" model based on relational tables to ease PBE development for a
wide but restrained range of domains. Implementing PBE tools with Bee only
requires adapting domain-specific data entities and user actions to tables,
with no need to design a domain-specific language or an efficient synthesis
algorithm. The synthesis algorithm of Bee exploits bidirectional searching and
constraint-solving techniques to address the challenge of value computation
nested in table transformation. We evaluated Bee's effectiveness on 64 PBE
tasks from three different domains and usability with a human study of 12
participants. Evaluation results show that Bee is easier to learn and use than
the state-of-the-art PBE framework, and the bidirectional algorithm achieves
comparable performance to domain-specifically optimized synthesizers.Comment: Accepted by ACM Transactions on Software Engineering and Methodolog
Understanding Translationese in Cross-Lingual Summarization
Given a document in a source language, cross-lingual summarization (CLS) aims
at generating a concise summary in a different target language. Unlike
monolingual summarization (MS), naturally occurring source-language documents
paired with target-language summaries are rare. To collect large-scale CLS
data, existing datasets typically involve translation in their creation.
However, the translated text is distinguished from the text originally written
in that language, i.e., translationese. In this paper, we first confirm that
different approaches of constructing CLS datasets will lead to different
degrees of translationese. Then we systematically investigate how
translationese affects CLS model evaluation and performance when it appears in
source documents or target summaries. In detail, we find that (1) the
translationese in documents or summaries of test sets might lead to the
discrepancy between human judgment and automatic evaluation; (2) the
translationese in training sets would harm model performance in real-world
applications; (3) though machine-translated documents involve translationese,
they are very useful for building CLS systems on low-resource languages under
specific training strategies. Lastly, we give suggestions for future CLS
research including dataset and model developments. We hope that our work could
let researchers notice the phenomenon of translationese in CLS and take it into
account in the future.Comment: Accepted to the Findings of EMNLP 202
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