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Found 18 papers, 3 papers with code
FP8-BERT: Post-Training Quantization for Transformer
Transformer-based models, such as BERT, have been widely applied in a wide range of natural language processing tasks.
S4: a High-sparsity, High-performance AI Accelerator
And the degree of sparsity one can exploit has become higher as larger model sizes have been considered along with the trend of pre-training giant models.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
In particular, RGE is shown to achieve \emph{(quasi-)linear scalability} with respect to the number and the size of the graphs.
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.
From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features
Graph kernels are one of the most important methods for graph data analysis and have been successfully applied in diverse applications.
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
We consider a generalization of mixed regression where the response is an additive combination of several mixture components.
Random Warping Series: A Random Features Method for Time-Series Embedding
The proposed kernel does not suffer from the issue of diagonal dominance while naturally enjoys a \emph{Random Features} (RF) approximation, which reduces the computational complexity of existing DTW-based techniques from quadratic to linear in terms of both the number and the length of time-series.
Loss Decomposition for Fast Learning in Large Output Spaces
For problems with large output spaces, evaluation of the loss function and its gradient are expensive, typically taking linear time in the size of the output space.
Scalable Spectral Clustering Using Random Binning Features
Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
Ranked #5 on
Image/Document Clustering
on pendigits
D2KE: From Distance to Kernel and Embedding
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more naturally than a feature representation.
Latent Feature Lasso
The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of latent features.
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization
We consider the popular problem of sparse empirical risk minimization with linear predictors and a large number of both features and observations.
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
In this work, we show that, by decomposing training of Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace expensive structured oracle with Factorwise Maximization Oracle (FMO) that allows efficient implementation of complexity sublinear to the factor domain.
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
In this work, we show that a margin-maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor.
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
Over the past decades, Linear Programming (LP) has been widely used in different areas and considered as one of the mature technologies in numerical optimization.
A Dual Augmented Block Minimization Framework for Learning with Limited Memory
In past few years, several techniques have been proposed for training of linear Support Vector Machine (SVM) in limited-memory setting, where a dual block-coordinate descent (dual-BCD) method was used to balance cost spent on I/O and computation.
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
State of the art statistical estimators for high-dimensional problems take the form of regularized, and hence non-smooth, convex programs.
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
In this paper, we propose a Sparse Random Feature algorithm, which learns a sparse non-linear predictor by minimizing an $\ell_1$-regularized objective function over the Hilbert Space induced from kernel function.
