An online SAR focusing framework using state-space models processes raw data line-by-line with 70x lower latency and 130x lower memory than block-based DSP while supporting downstream tasks.
Gomez, Lukasz Kaiser, and Illia Polosukhin
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A framework quantifies DNN complexity via tensor operations, links 40 years of breakthroughs to complexity increases, and releases a dataset of 3000+ unexplored high-complexity architectures.
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
In deep Transformers using bidirectional prefix masks, implicit reasoning on Horn clauses matches explicit CoT performance across topologies and widths, but CoT is still required for depth extrapolation.
citing papers explorer
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Learning to Focus Synthetic Aperture Radar On-line with State-Space Models
An online SAR focusing framework using state-space models processes raw data line-by-line with 70x lower latency and 130x lower memory than block-based DSP while supporting downstream tasks.
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On the Architectural Complexity of Neural Networks
A framework quantifies DNN complexity via tensor operations, links 40 years of breakthroughs to complexity increases, and releases a dataset of 3000+ unexplored high-complexity architectures.
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Gaussian Relational Graph Transformer
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
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The Scaling Properties of Implicit Deductive Reasoning in Transformers
In deep Transformers using bidirectional prefix masks, implicit reasoning on Horn clauses matches explicit CoT performance across topologies and widths, but CoT is still required for depth extrapolation.