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Random features for kernel approximation: A survey on algorithms, theory, and beyond
The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
One-pass distribution sketch for measuring data heterogeneity in federated learning
Federated learning (FL) is a machine learning paradigm where multiple client devices train
models collaboratively without data exchange. Data heterogeneity problem is naturally …
models collaboratively without data exchange. Data heterogeneity problem is naturally …
Smooth flip** probability for differential private sign random projection methods
We develop a series of differential privacy (DP) algorithms from a family of random
projection (RP) and sign random projection (SignRP) methods. We first show how to …
projection (RP) and sign random projection (SignRP) methods. We first show how to …
Learning a fourier transform for linear relative positional encodings in transformers
We propose a new class of linear Transformers called FourierLearner-Transformers (FLTs),
which incorporate a wide range of relative positional encoding mechanisms (RPEs). These …
which incorporate a wide range of relative positional encoding mechanisms (RPEs). These …
Low-precision arithmetic for fast Gaussian processes
Low precision arithmetic has had a transformative effect on the training of neural networks,
reducing computation, memory and energy requirements. However, despite their promise …
reducing computation, memory and energy requirements. However, despite their promise …
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps
We propose a normative model for spatial representation in the hippocampal formation that
combines optimality principles, such as maximizing coding range and spatial information per …
combines optimality principles, such as maximizing coding range and spatial information per …
One-sketch-for-all: Non-linear random features from compressed linear measurements
The commonly used Gaussian kernel has a tuning parameter $\gamma $. This makes the
design of quantization schemes for random Fourier features (RFF) challenging, which is a …
design of quantization schemes for random Fourier features (RFF) challenging, which is a …
Random matrices in service of ml footprint: ternary random features with no performance loss
In this article, we investigate the spectral behavior of random features kernel matrices of the
type ${\bf K}=\mathbb {E} _ {{\bf w}}\left [\sigma\left ({\bf w}^{\sf T}{\bf x} _i\right)\sigma\left ({\bf …
type ${\bf K}=\mathbb {E} _ {{\bf w}}\left [\sigma\left ({\bf w}^{\sf T}{\bf x} _i\right)\sigma\left ({\bf …
Signrff: Sign random fourier features
The industry practice has been moving to embedding based retrieval (EBR). For example, in
many applications, the embedding vectors are trained by some form of two-tower models …
many applications, the embedding vectors are trained by some form of two-tower models …
Retaining knowledge for learning with dynamic definition
Abstract Machine learning models are often deployed in settings where they must be
constantly updated in response to the changes in class definitions while retaining high …
constantly updated in response to the changes in class definitions while retaining high …