112 citations · 340 across the 21 of their papers we have counts for
21 papers
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Ziling Cheng, Meng Cao, Leila Pishdad +2
Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics…
An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation
Yuqiao Wen, Yongchang Hao, Yanshuai Cao +1
Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as…
Hierarchical Neural Data Synthesis for Semantic Parsing
Wei Yang, Peng Xu, Yanshuai Cao
Semantic parsing datasets are expensive to collect. Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily…
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
Peng Xu, Wenjie Zi, Hamidreza Shahidi +7
A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough…
A Globally Normalized Neural Model for Semantic Parsing
Chenyang Huang, Wei Yang, Yanshuai Cao +2
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued s…
Optimizing Deeper Transformers on Small Datasets
Peng Xu, Dhruv Kumar, Wei Yang +6
It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layer…
Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang, Alireza Makhzani, Yanshuai Cao +1
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood i…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs
Teng Long, Yanshuai Cao, Jackie Chi Kit Cheung
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of na…
Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
Yanshuai Cao, Peng Xu
In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inducti…
On Variational Learning of Controllable Representations for Text without Supervision
Peng Xu, Jackie Chi Kit Cheung, Yanshuai Cao
The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent s…
A Cross-Domain Transferable Neural Coherence Model
Peng Xu, Hamidreza Saghir, Jin Sung Kang +4
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain d…
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Junfeng Wen, Yanshuai Cao, Ruitong Huang
Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks.…
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui +1
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in…
Adversarial Contrastive Estimation
Avishek Joey Bose, Huan Ling, Yanshuai Cao
Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddin…
Implicit Manifold Learning on Generative Adversarial Networks
Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau +1
This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a subman…
Automatic Selection of t-SNE Perplexity
Yanshuai Cao, Luyu Wang
t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter t…
Transductive Log Opinion Pool of Gaussian Process Experts
Yanshuai Cao, David J. Fleet
We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test…
Adversarial Manipulation of Deep Representations
Sara Sabour, Yanshuai Cao, Fartash Faghri +1
We show that the representation of an image in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations t…
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
Yanshuai Cao, David J. Fleet
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties tha…
Efficient Optimization for Sparse Gaussian Process Regression
Yanshuai Cao, Marcus A. Brubaker, David J. Fleet +1
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates an inducing set a…