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Found 20 papers, 7 papers with code
POSQA: Probe the World Models of LLMs with Size Comparisons
Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment.
FireAct: Toward Language Agent Fine-tuning
Recent efforts have augmented language models (LMs) with external tools or environments, leading to the development of language agents that can reason and act.
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Duet: efficient and scalable hybriD neUral rElation undersTanding
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark
Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks.
Pre-trained Language Models as Re-Annotators
Annotation noise is widespread in datasets, but manually revising a flawed corpus is time-consuming and error-prone.
SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimation
To the best of our knowledge, this is the first work to show that transformer-based networks can attain state-of-the-art performance in real-time in the single image depth estimation field.
Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes
This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization
Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm.
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
Photometric loss is widely used for self-supervised depth and egomotion estimation.
Non-iterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissue
In this paper, we propose a novel non-iterative algorithm to simultaneously estimate optimal rigid transformation for serial section images, which is a key component in volume reconstruction of serial sections of biological tissue.
How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context
We study the influence of context on sentence acceptability.
Early Rumour Detection
Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them.
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities.
Hierarchical Spatial Transformer Network
Computer vision researchers have been expecting that neural networks have spatial transformation ability to eliminate the interference caused by geometric distortion for a long time.
Estimation of Human Body Shape and Posture Under Clothing
Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans.
Finite Element Based Tracking of Deforming Surfaces
Our method combines the use of prior information on the geometry of the object modeled by a smooth template and the use of a linear finite element method to predict the deformation.
Fully Automatic Expression-Invariant Face Correspondence
The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan.
