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Found 13 papers, 5 papers with code
Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints
Self-training based on pseudo-labels has emerged as a dominant approach for addressing conditional distribution shifts in unsupervised domain adaptation (UDA) for semantic segmentation problems.
DSI++: Updating Transformer Memory with New Documents
In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents.
An Introduction to Lifelong Supervised Learning
Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP.
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning but also its potential to reduce energy waste by obviating excessive model re-training.
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.
Improving Compositional Generalization with Self-Training for Data-to-Text Generation
Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs).
Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints
Crucially, the objectness constraint is agnostic to the ground-truth semantic segmentation labels and, therefore, remains appropriate for unsupervised adaptation settings.
Efficient Meta Lifelong-Learning with Limited Memory
State-of-the-art lifelong language learning methods store past examples in episodic memory and replay them at both training and inference time.
Learning Rhyming Constraints using Structured Adversaries
Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry.
Spatial Co-location Pattern Mining - A new perspective using Graph Database
Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places.
Databases Distributed, Parallel, and Cluster Computing
Towards Semi-Supervised Learning for Deep Semantic Role Labeling
The paper proposes a semi-supervised semantic role labeling method that outperforms the state-of-the-art in limited SRL training corpora.
Gradient-based Inference for Networks with Output Constraints
Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output structures.
