# Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning

arXiv: https://arxiv.org/abs/1802.00662 · HTML: https://mlsanity.com/paper/1802.00662/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 27 of 27)

- [SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations](https://mlsanity.com/paper/2105.02711/) (2021)
- [Change Matters: Medication Change Prediction with Recurrent Residual Networks](https://mlsanity.com/paper/2105.01876/) (2021)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Conditional Generation Net for Medication Recommendation](https://mlsanity.com/paper/2202.06588/) (2022)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- [SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation](https://mlsanity.com/paper/2309.05675/) (2023)
- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation](https://mlsanity.com/paper/2104.11026/) (2021)
- [StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation](https://mlsanity.com/paper/2308.16781/) (2023)
- [Natural Language-Assisted Multi-modal Medication Recommendation](https://mlsanity.com/paper/2501.07166/) (2025)
- [CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning](https://mlsanity.com/paper/2403.00880/) (2024)
- [HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships](https://mlsanity.com/paper/2603.18459/) (2026)
- [HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System](https://mlsanity.com/paper/2206.02628/) (2022)
- [Knowledge-Driven New Drug Recommendation](https://mlsanity.com/paper/2210.05572/) (2022)
- [DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning](https://mlsanity.com/paper/2305.19604/) (2023)
- [A Framework for Inference Inspired by Human Memory Mechanisms](https://mlsanity.com/paper/2310.09297/) (2023)
- [Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting](https://mlsanity.com/paper/2510.01206/) (2025)
- [ARMR: Adaptively Responsive Network for Medication Recommendation](https://mlsanity.com/paper/2507.04428/) (2025)
- [Traceable Drug Recommendation over Medical Knowledge Graphs](https://mlsanity.com/paper/2510.27274/) (2025)
- [KnowAugNet: Multi-Source Medical Knowledge Augmented Medication Prediction Network with Multi-Level Graph Contrastive Learning](https://mlsanity.com/paper/2204.11736/) (2022)
- [ALGNet: Attention Light Graph Memory Network for Medical Recommendation System](https://mlsanity.com/paper/2312.08377/) (2023)
- [Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory](https://mlsanity.com/paper/2404.11870/) (2024)
- [BernGraph: Probabilistic Graph Neural Networks for EHR-based Medication Recommendations](https://mlsanity.com/paper/2408.09410/) (2024)
- [Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation](https://mlsanity.com/paper/2504.18096/) (2025)
- [GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2505.12888/) (2025)
- [CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion](https://mlsanity.com/paper/2604.16411/) (2026)
- [GRAIN: Molecules Are Not the Right Granularity -- Active-Ingredient Modeling for Safe Medication Recommendation](https://mlsanity.com/paper/2608.00098/) (2026)

## Abstract (arXiv)

One of the core tasks in multi-view learning is to capture relations among views. For sequential data, the relations not only span across views, but also extend throughout the view length to form long-term intra-view and inter-view interactions. In this paper, we present a new memory augmented neural network model that aims to model these complex interactions between two asynchronous sequential views. Our model uses two encoders for reading from and writing to two external memories for encoding input views. The intra-view interactions and the long-term dependencies are captured by the use of memories during this encoding process. There are two modes of memory accessing in our system: late-fusion and early-fusion, corresponding to late and early inter-view interactions. In the late-fusion mode, the two memories are separated, containing only view-specific contents. In the early-fusion mode, the two memories share the same addressing space, allowing cross-memory accessing. In both cases, the knowledge from the memories will be combined by a decoder to make predictions over the output space. The resulting dual memory neural computer is demonstrated on a comprehensive set of experiments, including a synthetic task of summing two sequences and the tasks of drug prescription and disease progression in healthcare. The results demonstrate competitive performance over both traditional algorithms and deep learning methods designed for multi-view problems.