Key papers

• Online Safety Monitoring for LLMs (2026)
  Mona Schirmer et al.
  3.51
• N-ary Error Correcting Coding Scheme (2016)
  Joey Tianyi Zhou et al.
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• Deep Probabilistic Programming (2017)
  Dustin Tran et al.
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• Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation (2018)
  Jack Kosaian et al.
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• An Introduction to Probabilistic Programming (2018)
  Jan-Willem van de Meent et al.
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• LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks (2018)
  Yihan Jiang et al.
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• Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs (2019)
  Yaniv David et al.
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• Predicting Algorithm Classes for Programming Word Problems (2019)
  Vinayak Athavale et al.
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• LUTNet: Rethinking Inference in FPGA Soft Logic (2019)
  Erwei Wang et al.
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• Learning based Methods for Code Runtime Complexity Prediction (2019)
  Jagriti Sikka et al.
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• Learning to Fix Build Errors with Graph2Diff Neural Networks (2019)
  Daniel Tarlow et al.
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• Beyond Graph Neural Networks with Lifted Relational Neural Networks (2020)
  Gustav Sourek et al.
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• Applications of Deep Neural Networks with Keras (2020)
  Jeff Heaton
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• Solving Mixed Integer Programs Using Neural Networks (2020)
  Vinod Nair et al.
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• HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search (2021)
  Xiangzhong Luo et al.
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• LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes (2021)
  Aditya Kusupati et al.
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• Nonparametric Hamiltonian Monte Carlo (2021)
  Carol Mak et al.
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• Learning Primal Heuristics for Mixed Integer Programs (2021)
  Yunzhuang Shen et al.
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• ProductAE: Towards Training Larger Channel Codes based on Neural Product Codes (2021)
  Mohammad Vahid Jamali et al.
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• Neur2SP: Neural Two-Stage Stochastic Programming (2022)
  Justin Dumouchelle et al.
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• Machines Explaining Linear Programs (2022)
  David Steinmann and Matej Ze\v{c}evi\'c and Devendra Singh Dhami and Kristian Kersting
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• LogGD:Detecting Anomalies from System Logs by Graph Neural Networks (2022)
  Yongzheng Xie et al.
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• On Representing Linear Programs by Graph Neural Networks (2022)
  Ziang Chen et al.
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• A Scalable Graph Neural Network Decoder for Short Block Codes (2022)
  Kou Tian et al.
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• Symbolic Regression for PDEs using Pruned Differentiable Programs (2023)
  Ritam Majumdar et al.
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• Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs (2023)
  Taehyun Yoon et al.
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• Boosting Learning for LDPC Codes to Improve the Error-Floor Performance (2023)
  Hee-Youl Kwak et al.
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• Nebula: Self-Attention for Dynamic Malware Analysis (2023)
  Dmitrijs Trizna et al.
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• Toward TransfORmers: Revolutionizing the Solution of Mixed Integer Programs with Transformers (2024)
  Joshua F. Cooper et al.
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• MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems (2024)
  Flavio Ponzina and Tajana Rosing
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• CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes (2024)
  Seong-Joon Park et al.
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• Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming (2024)
  Tianyang Wang et al.
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• Neural Window Decoder for SC-LDPC Codes (2024)
  Dae-Young Yun et al.
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• An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling (2024)
  Linxin Yang et al.
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