Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Let the Flows T ell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Neural Information Processing Systems
GFlowNet policies can efficiently find high-quality solutions.
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Attention layers--which map a sequence of inputs to a sequence of outputs--are core building blocks of the Transformer architecture which has achieved significant
Neural Information Processing Systems
We show that such a random-feature attention layer can express a broad class of target functions that are permutation invariant to the key vectors.
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Supra-LaplacianEncodingfor TransformeronDynamicGraphs
Neural Information Processing Systems
GTs directly connect nodes, using the graph structure asasoft bias through positionalencoding[37].
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