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Journal of Artificial Intelligence Research

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PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation

Large pretrained language models such as BERT suffer from slow inference and high memory usage, due to their huge size. Recent approaches to compressing BERT rely on iterative pruning and knowledge distillation, which, however, are often too complicated and computationally intensive. This paper proposes a novel semi-structured one-shot pruning method for BERT, called Permutat...


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Label-Aware Pseudo-Training Sample Generation for Text Classification

Deep learning models excel in various Natural Language Processing (NLP) tasks, but their performance (excluding approaches like zero-shot learning or few-shot learning) relies on ample data, posing challenges in fields with limited datasets. To address the poverty in the size of training data, a number of approaches could be taken, such as multi-task learning and data augment...


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Procedural Fairness in Machine Learning

Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of procedural fairnes...


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General Supervised Learning Framework for Open World Classification

In open-world supervised learning for classification, the training data is incomplete with respect to the full set of relevant classes in the application domain. Most existing research on this problem focuses on computer vision, and many of the proposed methodologies are intrinsically tied to specific machine learning algorithms or data types. However, real-world open-world s...


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Revisiting SAT-based Solvers: MaxSAT Rules and Core Sequences

In this paper, we revisit the state-of-the-art of MaxSAT solving. We focus on SAT-based MaxSAT solving algorithms, mainly on Core-guided MaxSAT solvers. We show how to describe Core-guided solvers with Non-CNF MaxSAT rules plus the Extension rule. Equipped with these rules, we show how to apply them alternatively to obtain new Core-guided MaxSAT solvers. Since Core-guided sol...


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