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cs.LG2025
Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
Yeskendir Koishekenov, Aldo Lipani, Nicola Cancedda
Most efforts to improve the reasoning capabilities of large language models (LLMs) involve either scaling the number of parameters and the size of training data, or scaling inferen…
cs.LG2023
An Exploration of Conditioning Methods in Graph Neural Networks
Yeskendir Koishekenov, Erik J. Bekkers
The flexibility and effectiveness of message passing based graph neural networks (GNNs) induced considerable advances in deep learning on graph-structured data. In such approaches,…
cs.LG2023★ 3 cited
Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings
Yeskendir Koishekenov
Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as th…