29 citations · 56 across the 16 of their papers we have counts for
8 papers · 1 filter
Accounting for Work Zone Disruptions in Traffic Flow Forecasting
Yuanjie Lu, Amarda Shehu, David Lattanzi
Traffic speed forecasting is an important task in intelligent transportation system management. The objective of much of the current computational research is to minimize the diffe…
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms
Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
Despite an extensive body of literature on deep learning optimization, our current understanding of what makes an optimization algorithm effective is fragmented. In particular, we…
Multi-objective Deep Data Generation with Correlated Property Control
Shiyu Wang, Xiaojie Guo, Xuanyang Lin +11
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular desig…
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets
Parastoo Kamranfar, David Lattanzi, Amarda Shehu +1
Detecting anomalies over real-world datasets remains a challenging task. Data annotation is an intensive human labor problem, particularly in sequential datasets, where the start a…
Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks
Anowarul Kabir, Amarda Shehu
The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Rece…
Interpretable Molecular Graph Generation via Monotonic Constraints
Yuanqi Du, Xiaojie Guo, Amarda Shehu +1
Designing molecules with specific properties is a long-lasting research problem and is central to advancing crucial domains such as drug discovery and material science. Recent adva…
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
Yuanjie Lu, Parastoo Kamranfar, David Lattanzi +1
Forecasting traffic flows is a central task in intelligent transportation system management. Graph structures have shown promise as a modeling framework, with recent advances in sp…
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…