Ling Wang

Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

2015 · arXiv (Cornell University)

Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez, Silvio Amir, Luís Marujo, Tiago Luís. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

2016

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is …

Learning and Evaluating General Linguistic Intelligence

2019 · arXiv (Cornell University)

We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural …

Random Walk Mutation-based DE with EDA for Nonlinear Equations Systems

2019

Finding multiple roots of nonlinear equations systems (NESs) in a single run is an important yet difficult task. It requires to keep a balance between explorative and exploitative traits. In this paper, we present a …

A Mutual Information Maximization Perspective of Language Representation Learning

2020 · arXiv (Cornell University)

We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative …

Dynamic Multi-Indicator Fusion Model for Real-Time Prediction Analysis

2024

Power load forecasting is influenced by various factors, including meteorological, economic, and social factors. Considering all influencing factors will significantly increase the model complexity, affect accuracy and prediction timeliness. In addition, as time, region, and …

Diverse Transformation-Augmented Graph Tensor Convolutional Network for Dynamic Graph Representation Learning

2024

A dynamic graphs (DG) is frequently adopted to describe the evolving interactions between nodes in real-world applications such as device communication networks. Temporal patterns are the natural characteristics of DG and are also the key …

Research on Automation of Rural Landscape Design Based on Convolutional Neural Network and Generative Adversarial Network

2026 · Lecture notes in electrical engineering

Driven by the rural revitalization strategy, rural landscape design is entering a new stage. It needs to face the dual challenges of innovation and sustainability. This study explores the application of deep learning in rural …

Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

2017

Solving algebraic word problems requires executing a series of arithmetic operations-a program-to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make …

Neural Network-Based Abstract Generation for Opinions and Arguments

2016

We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. …