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Found 8 papers, 3 papers with code
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
Incorporating Experts' Judgment into Machine Learning Models
However, in some cases, domain experts might have a judgment about the expected outcome that might conflict with the prediction of ML models.
RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformer
Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance.
Reproducible and Portable Big Data Analytics in the Cloud
To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds.
Scalable and Hybrid Ensemble-Based Causality Discovery
Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience.
Semantic Network Interpretation
For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes.
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
Rather than representing an object by regions aligned to image axes, the proposed representation characterizes appearance relative to the object's pose using pose-aligned patches whose features are robust to variations in pose, scale and rotation.
Ranked #17 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Dogs
