Lawrence Carin

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

2016 · International Conference on Artificial Intelligence and Statistics

Belief networks are commonly used generative models of data, but require expensive posterior estimation to train and test the model. Learning typically proceeds by posterior sampling, variational approximations, or recognition networks, combined with stochastic optimization. …

Learning Compressed Sentence Representations for On-Device Text Processing

2019 · arXiv (Cornell University)

Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise …

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability

2020 · arXiv (Cornell University)

We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decision boundaries at the output layer of the source model, …

GAN Memory with No Forgetting

2020 · arXiv (Cornell University)

As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfectly, no forgetting should be expected. Motivated by that, we propose a …

Learning Graphons via Structured Gromov-Wasserstein Barycenters

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory …

Differentiable Hierarchical Optimal Transport for Robust Multi-View Learning

2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Traditional multi-view learning methods often rely on two assumptions: ( i) the samples in different views are well-aligned, and ( ii) their representations obey the same distribution in a latent space. Unfortunately, these two assumptions …

Deconvolutional Latent-Variable Model for Text Sequence Matching

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence …

Cyclical Annealing Schedule: A Simple Approach to Mitigating

2019

Hao Fu, Chunyuan Li, Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and …

Deconvolutional Latent-Variable Model for Text Sequence Matching

2017 · arXiv (Cornell University)

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence …

Joint Embedding of Words and Labels for Text Classification

2018

Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

2018

Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). …

What Makes Good In-Context Examples for GPT-3?

2022

Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, Weizhu Chen. Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. 2022.