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Found 12 papers, 5 papers with code
MiniMax-01: Scaling Foundation Models with Lightning Attention
This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens.
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Specifically, skip-cross fusion strategy connects each layer to each layer in a feed-forward manner, and for each layer, the feature maps of all previous layers are used as input and its own feature maps are used as input to all subsequent layers for the other modality, enhancing feature propagation and multi-modal features fusion.
Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models.
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
However current research rarely studies the impact of different amounts of instruction data on model performance, especially in the real-world use cases.
Exploring ChatGPT's Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
The results on the test set show that ChatGPT's ranking preferences are consistent with human to a certain extent.
VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval
This paper introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder.
Multi-modal Fusion Technology based on Vehicle Information: A Survey
Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years.
Improving Visual-Semantic Embeddings by Learning Semantically-Enhanced Hard Negatives for Cross-modal Information Retrieval
Visual Semantic Embedding (VSE) aims to extract the semantics of images and their descriptions, and embed them into the same latent space for cross-modal information retrieval.
A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection
Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient.
Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)
This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.
Photometric Redshift Estimation
Cosmology and Nongalactic Astrophysics
Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods
Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life.
