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Aditya Joshi

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4 papers

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TIST Journal 2026 Journal Article

Spectraformer: A Unified Random Feature Framework for Transformer

Duke Nguyen
Du Yin
Aditya Joshi
Flora Salim

Linearization of attention using various kernel approximation and kernel learning techniques has shown promise. Past methods used a subset of combinations of component functions and weight matrices within the random feature paradigm. We identify the need for a systematic comparison of different combinations of weight matrices and component functions for attention learning in Transformer. Hence, we introduce Spectraformer, a unified framework for approximating and learning the kernel function in the attention mechanism of the Transformer. Our empirical results demonstrate, for the first time, that a random feature-based approach can achieve performance comparable to top-performing sparse and low-rank methods on the challenging Long-Range Arena benchmark. Thus, we establish a new state-of-the-art for random feature-based efficient Transformers. The framework also produces many variants that offer different advantages in accuracy, training time, and memory consumption. Our code is available at: https://github.com/cruiseresearchgroup/spectraformer.

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AAAI Conference 2026 Conference Paper

TRACE: Textual Relevance Augmentation and Contextual Encoding for Multimodal Hate Detection

Girish A. Koushik
Helen Treharne
Aditya Joshi
Diptesh Kanojia

Social media memes are a challenging domain for hate detection because they intertwine visual and textual cues into culturally nuanced messages. To tackle these challenges, we introduce TRACE, a hierarchical multimodal framework that leverages visually grounded context augmentation, along with a novel caption-scoring network to emphasize hate-relevant content, and parameter-efficient fine-tuning of CLIP’s text encoder. Our experiments demonstrate that selectively fine-tuning deeper text encoder layers significantly enhances performance compared to simpler projection-layer fine-tuning methods. Specifically, our framework achieves state-of-the-art accuracy (0.807) and F1-score (0.806) on the widely-used Hateful Memes dataset, matching the performance of considerably larger models while maintaining efficiency. Moreover, it achieves superior generalization on the MultiOFF offensive meme dataset (F1-score 0.673), highlighting robustness across meme categories. Additional analyses confirm that robust visual grounding and nuanced text representations significantly reduce errors caused by benign confounders. We publicly release our code to facilitate future research.

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AAAI Conference 2020 System Paper

‘Watch the Flu’: A Tweet Monitoring Tool for Epidemic Intelligence of Influenza in Australia

Brian Jin
Aditya Joshi
Ross Sparks
Stephen Wan
Cécile Paris
C Raina MacIntyre

‘Watch The Flu’ is a tool that monitors tweets posted in Australia for symptoms of influenza. The tool is a unique combination of two areas of artificial intelligence: natural language processing and time series monitoring, in order to assist public health surveillance. Using a real-time data pipeline, it deploys a web-based dashboard for visual analysis, and sends out emails to a set of users when an outbreak is detected. We expect that the tool will assist public health experts with their decision-making for disease outbreaks, by providing them insights from social media.

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AAAI Conference 2017 System Paper

Sarcasm Suite: A Browser-Based Engine for Sarcasm Detection and Generation

Aditya Joshi
Diptesh Kanojia
Pushpak Bhattacharyya
Mark Carman

Sarcasm Suite is a browser-based engine that deploys ﬁve of our past papers in sarcasm detection and generation. The sarcasm detection modules use four kinds of incongruity: sentiment incongruity, semantic incongruity, historical context incongruity and conversational context incongruity. The sarcasm generation module is a chatbot that responds sarcastically to user input. With a visually appealing interface that indicates predictions using ‘faces’ of our co-authors from our past papers, Sarcasm Suite is our ﬁrst demonstration of our work in computational sarcasm.

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