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Found 10 papers, 2 papers with code
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
State-space models (SSMs), such as Mamba Gu & Dao (2034), have been proposed as alternatives to Transformer networks in language modeling, by incorporating gating, convolutions, and input-dependent token selection to mitigate the quadratic cost of multi-head attention.
Balanced Group Convolution: An Improved Group Convolution Based on Approximability Estimates
The performance of neural networks has been significantly improved by increasing the number of channels in convolutional layers.
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions
Our goal is to accurately recover a \new{parameter vector $w$ such that the} function $g(w \cdot x)$ \new{has} arbitrarily small error when compared to the true values $g(w^* \cdot x)$, rather than the noisy measurements $y$.
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
We propose a new training algorithm, named DualFL (Dualized Federated Learning), for solving distributed optimization problems in federated learning.
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning.
A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators
From the drift and diffusion terms of the Fokker--Planck equation, unknown parameters of the system are identified.
Two-level Group Convolution
In this paper, we propose a new convolution methodology called ``two-level'' group convolution that is robust with respect to the increase of the number of groups and suitable for multi-GPU parallel computation.
ReLU Regression with Massart Noise
This supervised learning task is efficiently solvable in the realizable setting, but is known to be computationally hard with adversarial label noise.
Parareal Neural Networks Emulating a Parallel-in-time Algorithm
We observe that layers of DNN can be interpreted as the time step of a time-dependent problem and can be parallelized by emulating a parallel-in-time algorithm called parareal.
On Robust Mean Estimation under Coordinate-level Corruption
We study the problem of robust mean estimation and introduce a novel Hamming distance-based measure of distribution shift for coordinate-level corruptions.
