Fourier circuits in neural networks: Unlocking the potential of large language models in mathematical reasoning and modular arithmetic
In the evolving landscape of machine learning, a pivotal challenge lies in deciphering the
internal representations harnessed by neural networks and Transformers. Building on recent …
internal representations harnessed by neural networks and Transformers. Building on recent …
Federated Empirical Risk Minimization via Second-Order Method
Many convex optimization problems with important applications in machine learning are
formulated as empirical risk minimization (ERM). There are several examples: linear and …
formulated as empirical risk minimization (ERM). There are several examples: linear and …
Fast DFT Computation for Signals with Structured Support
CR Pochimireddy, A Siripuram… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Suppose an length signal has known frequency support of size. Given access to samples of
this signal, how fast can we compute the DFT? The answer to this question depends on the …
this signal, how fast can we compute the DFT? The answer to this question depends on the …
Numerical Stability of DFT Computation for Signals with Structured Support
We consider the problem of building numerically stable algorithms for computing Discrete
Fourier Transform (DFT) of $ N $-length signals with known frequency support of size $ k …
Fourier Transform (DFT) of $ N $-length signals with known frequency support of size $ k …