1 citations · 3 across the 17 of their papers we have counts for
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EvoMAS: Evolutionary Generation of Multi-Agent Systems
Yuntong Hu, Yuting Zhang, Matthew Trager +4
Large language model (LLM)-based multi-agent systems (MAS) show strong promise for complex reasoning, planning, and tool-augmented tasks, but designing effective MAS architectures…
Algebra Unveils Deep Learning -- An Invitation to Neuroalgebraic Geometry
Giovanni Luca Marchetti, Vahid Shahverdi, Stefano Mereta +2
In this position paper, we promote the study of function spaces parameterized by machine learning models through the lens of algebraic geometry. To this end, we focus on algebraic…
Geometry and Optimization of Shallow Polynomial Networks
Yossi Arjevani, Joan Bruna, Joe Kileel +2
We study shallow neural networks with monomial activations and output dimension one. The function space for these models can be identified with a set of symmetric tensors with boun…
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation
Lawrence Stewart, Matthew Trager, Sujan Kumar Gonugondla +1
Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model.In this work, we explore…
Compositional Structures in Neural Embedding and Interaction Decompositions
Matthew Trager, Alessandro Achille, Pramuditha Perera +2
We describe a basic correspondence between linear algebraic structures within vector embeddings in artificial neural networks and conditional independence constraints on the probab…
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
Luca Zancato, Arjun Seshadri, Yonatan Dukler +6
We describe a family of architectures to support transductive inference by allowing memory to grow to a finite but a-priori unknown bound while making efficient use of finite resou…