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Found 10 papers, 3 papers with code
Precision Neural Network Quantization via Learnable Adaptive Modules
Quantization Aware Training (QAT) is a neural network quantization technique that compresses model size and improves operational efficiency while effectively maintaining model performance.
Randomized Kaczmarz Methods with Beyond-Krylov Convergence
In this paper, we introduce Kaczmarz++, an accelerated randomized block Kaczmarz algorithm that exploits outlying singular values in the input to attain a fast Krylov-style convergence.
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
ASkotch outperforms state-of-the-art KRR solvers on a testbed of 23 large-scale KRR regression and classification tasks derived from a wide range of application domains, demonstrating the superiority of full KRR over inducing points KRR.
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Our methods are based on constructing a low-rank Nystr\"om approximation to $A$ using sparse random matrix sketching.
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
In this paper, we propose HERTA: a High-Efficiency and Rigorous Training Algorithm for Unfolded GNNs that accelerates the whole training process, achieving a nearly-linear time worst-case training guarantee.
Solving Dense Linear Systems Faster Than via Preconditioning
We give a stochastic optimization algorithm that solves a dense $n\times n$ real-valued linear system $Ax=b$, returning $\tilde x$ such that $\|A\tilde x-b\|\leq \epsilon\|b\|$ in time: $$\tilde O((n^2+nk^{\omega-1})\log1/\epsilon),$$ where $k$ is the number of singular values of $A$ larger than $O(1)$ times its smallest positive singular value, $\omega < 2. 372$ is the matrix multiplication exponent, and $\tilde O$ hides a poly-logarithmic in $n$ factor.
CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models
In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters.
Federated Adversarial Learning: A Framework with Convergence Analysis
Unlike the convergence analysis in classical centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for three reasons: 1) the complexity of min-max optimization, 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation and 3) inter-client heterogeneity.
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
To address this, our main insight is to optimize over a continuous superset of sparse matrices with a fixed structure known as products of butterfly matrices.
Provable Federated Adversarial Learning via Min-max Optimization
Unlike the convergence analysis in centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for two reasons: 1) the complexity of min-max optimization, and 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation.
