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Found 18 papers, 8 papers with code
Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization
In this study, we evaluate four publicly available LLMs (GPT-4, Gemini-Pro, Llama3, and Mixtral), investigating their ability to comprehend utterances even in the presence of uncertainty and identify the relevant data context and visual tasks.
From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas
The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas.
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine Translation
Moreover, to enhance the diversity of the training data we maximize the variety of influences they have on the model by clustering on their gradients and resampling.
WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers
Non-native English speakers (NNES) face challenges in digital workspace communication (e. g., emails, Slack messages), often inadvertently translating expressions from their native languages, which can lead to awkward or incorrect usage.
ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
Generative image models have emerged as a promising technology to produce realistic images.
DiJiang: Efficient Large Language Models through Compact Kernelization
In an effort to reduce the computational load of Transformers, research on linear attention has gained significant momentum.
TutoAI: A Cross-domain Framework for AI-assisted Mixed-media Tutorial Creation on Physical Tasks
Mixed-media tutorials, which integrate videos, images, text, and diagrams to teach procedural skills, offer more browsable alternatives than timeline-based videos.
HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity.
PanGu-$π$: Enhancing Language Model Architectures via Nonlinearity Compensation
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
Mystique: Deconstructing SVG Charts for Layout Reuse
To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings.
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model
The Origin-Destination~(OD) networks provide an estimation of the flow of people from every region to others in the city, which is an important research topic in transportation, urban simulation, etc.
Improving speech translation by fusing speech and text
To tackle these gaps, we propose \textbf{F}use-\textbf{S}peech-\textbf{T}ext (\textbf{FST}), a cross-modal model which supports three distinct input modalities for translation: speech, text, and fused speech-text.
Transportation Scenario Planning with Graph Neural Networks
Providing efficient human mobility services and infrastructure is one of the major concerns of most mid-sized to large cities around the world.
The Volctrans Neural Speech Translation System for IWSLT 2021
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
Robust Graph Learning Under Wasserstein Uncertainty
To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Learning Geo-Contextual Embeddings for Commuting Flow Prediction
Then, an attention mechanism is proposed based on the framework of graph attention network (GAT) to capture the spatial correlations and encode geographic contextual information to embedding space.
Learning Style Similarity for Searching Infographics
Infographics are complex graphic designs integrating text, images, charts and sketches.
