568 research outputs found
Modular Blended Attention Network for Video Question Answering
In multimodal machine learning tasks, it is due to the complexity of the
assignments that the network structure, in most cases, is assembled in a
sophisticated way. The holistic architecture can be separated into several
logical parts according to the respective ends that the modules are devised to
achieve. As the number of modalities of information representation increases,
constructing ad hoc subnetworks for processing the data from divergent
modalities while mediating the fusion of different information types has become
a cumbersome and expensive problem. In this paper, we present an approach to
facilitate the question with a reusable and composable neural unit; by
connecting the units in series or parallel, the arduous network constructing of
multimodal machine learning tasks will be accomplished in a much
straightforward way. Additionally, through parameter sharing (weights
replication) among the units, the space complexity will be significantly
reduced. We have conducted experiments on three commonly used datasets; our
method achieves impressive performance compared to several video QA baselines.Comment: I will not add others' names since this work has not been publishe
GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
Large language models (LLMs) are becoming attractive as few-shot reasoners to
solve Natural Language (NL)-related tasks. However, there is still much to
learn about how well LLMs understand structured data, such as tables. While it
is true that tables can be used as inputs to LLMs with serialization, there is
a lack of comprehensive studies examining whether LLMs can truly comprehend
such data. In this paper, we try to understand this by designing a benchmark to
evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark
we create includes seven tasks, each with its own unique challenges, \eg, cell
lookup, row retrieval, and size detection. We conduct a series of evaluations
on GPT-3.5 and GPT-4. We find that the performance varied depending on several
input choices, including table input format, content order, role prompting, and
partition marks. Drawing from the insights gained through the benchmark
evaluations, we propose \textit{self-augmentation} for effective structural
prompting, such as critical value / range identification using LLMs' internal
knowledge. When combined with carefully chosen input choices, these structural
prompting methods lead to promising improvements in LLM performance on a
variety of tabular tasks, \eg, TabFact(),
HybridQA(), SQA(), Feverous(),
and ToTTo(). We believe that our benchmark and proposed
prompting methods can serve as a simple yet generic selection for future
research.Comment: This paper has been accepted as a full paper at WSDM 202
The Actor, Partner, Similarity Effects of Personality, and Interactions with Gender and Relationship Duration among Chinese Emerging Adults
Understanding personality effects and their role in influencing relationship quality, varied according to gender and relationship duration, could help us better understand close relationships. Participants were Chinese dating dyads and were asked to complete both the Big Five Inventory and Perceived Relationship Quality Component scales. Males and those who had a long-term relationship perceived better relationship quality; individuals who scored higher on agreeableness, conscientiousness, openness, and emotional stability enjoyed better relationship quality; gender and/or relationship duration moderated the actor effect of extraversion and the partner effects of conscientiousness, emotional stability, and openness on relationship quality. Regarding the profile similarity, those couples who were more dissimilar in their profile personality had better relationship quality, especially when they were in a relatively long-term relationship. Meanwhile, with an increase in profile similarity, the males' perceived relationship quality decreased
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
This paper aims to theoretically analyze the complexity of feature
transformations encoded in piecewise linear DNNs with ReLU layers. We propose
metrics to measure three types of complexities of transformations based on the
information theory. We further discover and prove the strong correlation
between the complexity and the disentanglement of transformations. Based on the
proposed metrics, we analyze two typical phenomena of the change of the
transformation complexity during the training process, and explore the ceiling
of a DNN's complexity. The proposed metrics can also be used as a loss to learn
a DNN with the minimum complexity, which also controls the over-fitting level
of the DNN and influences adversarial robustness, adversarial transferability,
and knowledge consistency. Comprehensive comparative studies have provided new
perspectives to understand the DNN
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