2 code implementations • 3 Oct 2023 • Kazem Meidani, Parshin Shojaee, Chandan K. Reddy, Amir Barati Farimani
To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
1 code implementation • 30 May 2023 • Zibo Liu, Parshin Shojaee, Chandan K Reddy
Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures.
1 code implementation • NeurIPS 2023 • Parshin Shojaee, Kazem Meidani, Amir Barati Farimani, Chandan K. Reddy
Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity, as external sources of knowledge into the transformer-based equation generation process.
1 code implementation • 31 Jan 2023 • Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy
It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs.
no code implementations • 23 Jul 2021 • Parshin Shojaee, Xiaoyu Chen, Ran Jin
Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most suitable patients.
no code implementations • 16 Dec 2020 • Manish Bansal, Parshin Shojaee
We consider a generalization of the classical planar maximum coverage location problem (PMCLP) in which partial coverage is allowed, facilities have adjustable quality of service (QoS) or service range, and demand zones and service zone of each facility are represented by two-dimensional spatial objects such as rectangles, circles, polygons, etc.
Optimization and Control Data Structures and Algorithms