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Found 23 papers, 9 papers with code
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
This study led to Pangu Ultra MoE, a sparse LLM with 718 billion parameters, and we conducted experiments on the model to verify the simulation results.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation.
Ensuring Consistency for In-Image Translation
Subsequently, a diffusion model trained for style-consistent text-image generation ensures uniformity in text style within images and preserves background details.
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
Retrieval-augmented generation systems rely on effective document retrieval capabilities.
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective
To mitigate this issue, we propose MVCL-MI (Maximization of Vision-Language Cross-Lingual Mutual Information), where a visual-text cross-lingual alignment is built by maximizing mutual information between the model's outputs and visual information.
Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
In this paper, we introduce and investigate a cross-lingual latent transplantation (XTransplant) framework, which aims to further exploit the model's internalized multilingual knowledge during inference and examine its effects on the multilingual capability and cultural adaptability of LLMs.
ToolACE: Winning the Points of LLM Function Calling
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability.
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations.
Concise and Precise Context Compression for Tool-Using Language Models
However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.
DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF
Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF.
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.
Stable Attribute Group Editing for Reliable Few-shot Image Generation
SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary.
Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency
Furthermore, we devise the spatio-temporal correlative map as a style-independent, global-aware regularization on the perceptual motion consistency.
Intrinsic Bias Identification on Medical Image Datasets
However, scientists and practitioners are difficult to identify implicit biases in the datasets, which causes lack of reliable unbias test datasets to valid models.
Attribute Group Editing for Reliable Few-shot Image Generation
Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs).
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19
Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage.
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model.
Learning Directional Feature Maps for Cardiac MRI Segmentation
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.
Face Detection Using Improved Faster RCNN
Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection.
Ranked #8 on
Face Detection
on WIDER Face (Hard)
