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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
portfolio
Tokdash - Token Usage Dashboard
A dashboard for monitoring and visualizing token usage across LLM API calls.
United Kingdom Flood Warning System
A flood warning system for the UK, built with Python for real-time monitoring of flood risk data.
Gesture Agent
An agent system that interprets and responds to gesture inputs using computer vision.
Cambridge 3F3 - Statistical Signal Processing
Comprehensive notes and materials for the Cambridge Engineering 3F3 Statistical Signal Processing course.
Cambridge 3F7 - Information Theory and Coding
Course notes for Cambridge Engineering 3F7 covering information theory, source coding, and channel coding.
Gene Expression Tumor Correlation Analysis
Data analysis project exploring correlations between gene expression levels and tumor characteristics.
Learning Concurrency in C++
Exercises and examples for learning concurrent programming patterns in C++.
Cambridge Blue Noise
Implementation and experiments with blue noise sampling algorithms.
publications
Method, System, Medium, and Electronic Device for Optimizing Fault-Tolerant Neural Network Structure
Published in China National Intellectual Property Administration
A method for optimizing fault-tolerant neural network architectures using Bayesian optimization, addressing resistance variation and bit-flip in ReRAM devices.
Recommended citation: N. Ye, Z. Fang, J. Mei. "Method, System, Medium, and Electronic Device for Optimizing Fault-Tolerant Neural Network Structure." CN Patent CN113,570,056 A, 2021.
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
Published in DAC 2021 (Design Automation Conference)
We propose BayesFT, a Bayesian optimization framework for designing fault-tolerant neural network architectures. Our approach efficiently searches the architecture space to find models that maintain performance under hardware faults, achieving state-of-the-art results on fault-tolerant neural network design.
Recommended citation: N. Ye*, J. Mei*, Z. Fang, Y. Zhang, Z. Zhang, H. Wu, X. Liang. "BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture." DAC 2021.
Fine-Grained Late-Interaction Multi-Modal Retrieval for Retrieval Augmented Visual Question Answering
Published in NeurIPS 2023
We propose FLMR, a fine-grained late-interaction multi-modal retrieval model for retrieval-augmented visual question answering. FLMR extends the ColBERT architecture to handle multi-modal queries and documents, enabling effective retrieval for knowledge-intensive VQA tasks.
Recommended citation: W. Lin, J. Chen, J. Mei, A. Coca, B. Byrne. "Fine-Grained Late-Interaction Multi-Modal Retrieval for Retrieval Augmented Visual Question Answering." NeurIPS 2023.
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Apparatus and Method for Streaming Automatic Speech Recognition
Published in European Patent Office
A novel non-attention-based transformer architecture for efficient on-device streaming automatic speech recognition, achieving a 50% reduction in model size and computation along with a 60% decrease in end-to-end latency.
Recommended citation: J. Mei, Z. Zhang. "Apparatus and Method for Streaming Automatic Speech Recognition." EP Patent EP4404187A1, 2024.
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata
Published in NAACL 2024 Main
We introduce Control-DAG, a constrained decoding method for non-autoregressive Directed Acyclic T5 using weighted finite state automata. This enables efficient constrained generation while maintaining the speed advantages of non-autoregressive models.
Recommended citation: J. Chen, W. Lin, J. Mei, B. Byrne. "Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata." NAACL 2024 Main.
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PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-Modal Retrievers
Published in ACL 2024 Main
We introduce PreFLMR, a large-scale pre-trained fine-grained late-interaction multi-modal retriever. By scaling up both the model and pre-training data, PreFLMR achieves state-of-the-art performance on a wide range of multi-modal retrieval benchmarks.
Recommended citation: W. Lin*, J. Mei*, J. Chen*, B. Byrne. "PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-Modal Retrievers." ACL 2024 Main.
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Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
Published in ACL 2024 Main
We propose RGCL, a retrieval-guided contrastive learning framework for hateful meme detection. By retrieving similar memes and using them as contrastive examples, our model learns more discriminative representations for distinguishing hateful from benign memes.
Recommended citation: J. Mei, J. Chen, W. Lin, B. Byrne, M. Tomalin. "Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning." ACL 2024 Main.
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Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
Published in EMNLP 2025 Main (Oral)
We propose a robust adaptation approach for large multimodal models in retrieval-augmented hateful meme detection. Our method effectively leverages retrieved examples to improve detection performance while maintaining robustness against noisy retrievals.
Recommended citation: J. Mei, J. Chen, G. Yang, W. Lin, B. Byrne. "Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection." EMNLP 2025 Main (Oral).
On Extending Direct Preference Optimization to Accommodate Ties
Published in NeurIPS 2025
We extend Direct Preference Optimization (DPO) to handle tied preferences, where annotators cannot distinguish between two responses. Our method provides a principled framework for incorporating ties into preference-based alignment of language models.
Recommended citation: J. Chen, G. Yang, W. Lin, J. Mei, B. Byrne. "On Extending Direct Preference Optimization to Accommodate Ties." NeurIPS 2025.
ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection
Published in ICLR 2026
We propose ExPO-HM, a novel explain-then-detect framework for hateful meme detection. By first generating explanations of why a meme might be hateful and then using these explanations to guide detection, our model achieves improved performance and interpretability.
Recommended citation: J. Mei, M. Sun, J. Chen, P. Qin, Y. Li, D. Chen, B. Byrne. "ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection." ICLR 2026.
According to Me: Long-Term Personalized Referential Memory QA
Published in arXiv preprint
We introduce ATM-Bench, a benchmark for long-term personalized referential memory question answering. Our work addresses the challenge of building AI systems that can maintain and reason over personalized memory across long-term interactions.
Recommended citation: J. Mei, J. Chen, G. Yang, X. Hou, M. Li, B. Byrne. "According to Me: Long-Term Personalized Referential Memory QA." arXiv preprint arXiv:2603.01990.
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Controllable Multi-label Video Safety Detection via Adaptive Tversky Policy Optimization
Published in arXiv preprint
A novel approach for controllable multi-label video safety detection using adaptive Tversky policy optimization, enabling fine-grained safety classification of video content.
Recommended citation: Controllable Multi-label Video Safety Detection via Adaptive Tversky Policy Optimization.
Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models
Published in ACL 2026 Main
We introduce RAD, a retrieval-augmented defense mechanism for preventing jailbreak attacks on large language models. Our approach adaptively retrieves relevant safety guidelines to provide controllable and effective jailbreak prevention.
Recommended citation: G. Yang, J. Chen, J. Mei, W. Lin, B. Byrne. "Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models." ACL 2026 Main.
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