618 citations · 994 across the 15 of their papers we have counts for
15 papers
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido +3
Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, us…
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Paul Soulos, Sudha Rao, Caitlin Smith +9
Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still impl…
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
Yichen Jiang, Asli Celikyilmaz, Paul Smolensky +7
Abstractive summarization, the task of generating a concise summary of input documents, requires: (1) reasoning over the source document to determine the salient pieces of informat…
Compositional Processing Emerges in Neural Networks Solving Math Problems
Jacob Russin, Roland Fernandez, Hamid Palangi +4
A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., g…
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
Hassan Akbari, Hamid Palangi, Jianwei Yang +6
Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi…
Novel Human-Object Interaction Detection via Adversarial Domain Generalization
Yuhang Song, Wenbo Li, Lei Zhang +6
We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios. The challenge…
HUBERT Untangles BERT to Improve Transfer across NLP Tasks
Mehrad Moradshahi, Hamid Palangi, Monica S. Lam +2
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model.…
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural Representations
Kezhen Chen, Qiuyuan Huang, Hamid Palangi +3
Generating formal-language programs represented by relational tuples, such as Lisp programs or mathematical operations, to solve problems stated in natural language is a challengin…
Learning Visual Relation Priors for Image-Text Matching and Image Captioning with Neural Scene Graph Generators
Kuang-Huei Lee, Hamid Palangi, Xi Chen +2
Grounding language to visual relations is critical to various language-and-vision applications. In this work, we tackle two fundamental language-and-vision tasks: image-text matchi…
Unified Vision-Language Pre-Training for Image Captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang +3
This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image cap…
Question-Answering with Grammatically-Interpretable Representations
Hamid Palangi, Paul Smolensky, Xiaodong He +1
We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In our application of TPRN, internal representations learned by end-to-end optimization in a deep neural…
Distributed Compressive Sensing: A Deep Learning Approach
Hamid Palangi, Rabab Ward, Li Deng
Various studies that address the compressed sensing problem with Multiple Measurement Vectors (MMVs) have been recently carried. These studies assume the vectors of the different c…
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Hamid Palangi, Li Deng, Yelong Shen +5
This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks with Long Short-Term Mem…
Semantic Modelling with Long-Short-Term Memory for Information Retrieval
H. Palangi, L. Deng, Y. Shen +5
In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike co…
Learning Input and Recurrent Weight Matrices in Echo State Networks
Hamid Palangi, Li Deng, Rabab K Ward
Echo State Networks (ESNs) are a special type of the temporally deep network model, the Recurrent Neural Network (RNN), where the recurrent matrix is carefully designed and both th…