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Found 13 papers, 3 papers with code
LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models
To this end, we proposed the layer pruning and normalized distillation for compressing diffusion models (LAPTOP-Diff).
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering
Scene-Text Visual Question Answering (ST-VQA) aims to understand scene text in images and answer questions related to the text content.
Optical Character Recognition
Optical Character Recognition (OCR)
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Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
More than Correlation: Do Large Language Models Learn Causal Representations of Space?
Recent work found high mutual information between the learned representations of large language models (LLMs) and the geospatial property of its input, hinting an emergent internal model of space.
Big Learning Expectation Maximization
Mixture models serve as one fundamental tool with versatile applications.
Underwater Sound Speed Profile Construction: A Review
The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method.
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers
In this work, we propose a method with a mixture-of-expert (MOE) controllers to align the text-guided capacity of diffusion models with different kinds of human instructions, enabling our model to handle various open-domain image manipulation tasks with natural language instructions.
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
To tackle the issue of 'guidance collapse' and enhance consistency, we propose a novel framework, dubbed CompoNeRF, by integrating an editable 3D scene layout with object specific and scene-wide guidance mechanisms.
TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection
In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum.
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data
Unfortunately, the original Bigraphical Lasso algorithm is not applicable in case of large p and n due to memory requirements.
When Creators Meet the Metaverse: A Survey on Computational Arts
The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity.
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by Nathan Kallus and "Learning optimal distributionally robust individualized treatment rules" by Weibin Mo, Zhengling Qi, and Yufeng Liu.
Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19.
