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Saed Rezayi

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3 papers

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AAAI Conference 2023 Short Paper

Learning Better Representations Using Auxiliary Knowledge

Saed Rezayi

Representation Learning is the core of Machine Learning and Artificial Intelligence as it summarizes input data points into low dimensional vectors. This low dimensional vectors should be accurate portrayals of the input data, thus it is crucial to find the most effective and robust representation possible for given input as the performance of the ML task is dependent on the resulting representations. In this summary, we discuss an approach to augment representation learning which relies on external knowledge. We briefly describe the shortcoming of the existing techniques and describe how an auxiliary knowledge source could result in obtaining improved representations.

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IJCAI Conference 2022 Conference Paper

AgriBERT: Knowledge-Infused Agricultural Language Models for Matching Food and Nutrition

Saed Rezayi
Zhengliang Liu
Zihao Wu
Chandra Dhakal
Bao Ge
Chen Zhen
Tianming Liu
Sheng Li

Pretraining domain-specific language models remains an important challenge which limits their applicability in various areas such as agriculture. This paper investigates the effectiveness of leveraging food related text corpora (e. g. , food and agricultural literature) in pretraining transformer-based language models. We evaluate our trained language model, called AgriBERT, on the task of semantic matching, i. e. , establishing mapping between food descriptions and nutrition data, which is a long-standing challenge in the agricultural domain. In particular, we formulate the task as an answer selection problem, fine-tune the trained language model with the help of an external source of knowledge (e. g. , FoodOn ontology), and establish a baseline for this task. The experimental results reveal that our language model substantially outperforms other language models and baselines in the task of matching food description and nutrition.

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AAAI Conference 2022 Short Paper

XDC: Adversarial Adaptive Cross Domain Face Clustering (Student Abstract)

Saed Rezayi
Handong Zhao
Sheng Li

In this work we propose a scheme, called XDC, that uses adversarial learning to train an adaptive cross domain clustering model. XDC trains a classifier on a labeled dataset and assigns labels to an unlabeled dataset. We benefit from adversarial learning such that the target dataset takes part in the training. We also use an existing image classifiers in a plugand-play fashion (i. e. , it can be replaced with any other image classifier). Unlike existing works we update the parameters of the encoder and expose the target dataset to the model during training. We apply our model on two face dataset and one non-face dataset and obtain comparable results with state-ofthe-art face clustering models.

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