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Found 8 papers, 4 papers with code
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System
Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages.
SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision
The latest industrial inference engines, such as FasterTransformer and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed.
Semantic Matching from Different Perspectives
In this paper, we pay attention to the issue which is usually overlooked, i. e., \textit{similarity should be determined from different perspectives}.
Topic Detection and Tracking with Time-Aware Document Embeddings
TDT systems aim to cluster a corpus of news articles by event, and in that context, stories that describe the same event are likely to have been written at around the same time.
TunaGAN: Interpretable GAN for Smart Editing
In this paper, we introduce a tunable generative adversary network (TunaGAN) that uses an auxiliary network on top of existing generator networks (Style-GAN) to modify high-resolution face images according to user's high-level instructions, with good qualitative and quantitative performance.
