179 research outputs found
The distinctions between CDM and gravity according Noether symmetry
Noether's theory offers us a useful tool to research the conserved quantities
and symmetries of the modified gravity theories, among which the theory,
a generally modified teleparallel gravity, has been proposed to account for the
dark energy phenomena. By the Noether symmetry approach, we investigate the
power-law, exponential and polynomial forms of theories. All forms of
concerned in this work possess the time translational symmetry, which is
related with energy condition or Hamilton constraint. In addition, we find out
that the performances of the power-law and exponential forms are not pleasing.
It is rational adding a linear term to as the most efficient
amendment to resemble the teleparallel gravity or General Relativity on small
scales, ie., the scale of the solar system. The corresponding Noether symmetry
indicates that only time translational symmetry remains. Through numerically
calculations and observational data-sets constraining, the optimal form is obtained, whose cosmological solution resembles the
standard CDM best with lightly reduced cosmic age which can be
alleviated by introducing another term. More important is that we find
the significant differences between CDM and gravity. The
CDM model has also two additional symmetries and corresponding
positive conserved quantities, except the two negative conserved quantities.Comment: 9 pages, 5 figures, 2 tables, typos corrected, Refs. added, accepted
by EPJ-
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Recent studies have revealed the intriguing few-shot learning ability of
pretrained language models (PLMs): They can quickly adapt to a new task when
fine-tuned on a small amount of labeled data formulated as prompts, without
requiring abundant task-specific annotations. Despite their promising
performance, most existing few-shot approaches that only learn from the small
training set still underperform fully supervised training by nontrivial
margins. In this work, we study few-shot learning with PLMs from a different
perspective: We first tune an autoregressive PLM on the few-shot samples and
then use it as a generator to synthesize a large amount of novel training
samples which augment the original training set. To encourage the generator to
produce label-discriminative samples, we train it via weighted maximum
likelihood where the weight of each token is automatically adjusted based on a
discriminative meta-learning objective. A classification PLM can then be
fine-tuned on both the few-shot and the synthetic samples with regularization
for better generalization and stability. Our approach FewGen achieves an
overall better result across seven classification tasks of the GLUE benchmark
than existing few-shot learning methods, improving no-augmentation methods by
5+ average points, and outperforming augmentation methods by 3+ average points.Comment: Code: https://github.com/yumeng5/FewGe
Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based
set expansion is to induce an extensive set of entities which share the same
semantic class (Country in this example) from a given corpus. Set expansion
benefits a wide range of downstream applications in knowledge discovery, such
as web search, taxonomy construction, and query suggestion. Existing
corpus-based set expansion algorithms typically bootstrap the given seeds by
incorporating lexical patterns and distributional similarity. However, due to
no negative sets provided explicitly, these methods suffer from semantic drift
caused by expanding the seed set freely without guidance. We propose a new
framework, Set-CoExpan, that automatically generates auxiliary sets as negative
sets that are closely related to the target set of user's interest, and then
performs multiple sets co-expansion that extracts discriminative features by
comparing target set with auxiliary sets, to form multiple cohesive sets that
are distinctive from one another, thus resolving the semantic drift issue. In
this paper we demonstrate that by generating auxiliary sets, we can guide the
expansion process of target set to avoid touching those ambiguous areas around
the border with auxiliary sets, and we show that Set-CoExpan outperforms strong
baseline methods significantly.Comment: WWW 202
- …