Publications related to End-to-end kernel learning via generative random Fourier features

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Mathias Josef Payer, Hui Peng

The Universal Serial Bus (USB) connects external devices to a host. This interface exposes the OS kernels and device drivers to attacks by malicious devices. Unfortunately, kernels and drivers were developed under a security model that implicitly trusts co ...

USENIX ASSOC2020

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Soumya Subhra Basu, Loris Gérard Duch

Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-Grained Reconfigurable Arrays (CGRAs) are good candidates for tackling these conflicting objectives because, than ...

2019

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Andy Roulin

Modern data-center network operating systems rely on proprietary user-space daemons wrapping SDKs from switch vendors. Linux-based variants of these operating systems have benefited from increasing and simplified dataplane offloading support in recent year ...

2018

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

Matthieu Martin Jean-André Simeoni

Real-life acquisition systems are fundamentally limited in their ability to reproduce point sources. For example, a point source object, a star say, observed with an optical telescope is blurred by the imperfect lenses composing the system. Mathematically, ...

2016

The Linux Scheduler: A Decade of Wasted Cores

Baptiste Joseph Eustache Lepers

As a central part of resource management, the OS thread scheduler must maintain the following, simple, invariant: make sure that ready threads are scheduled on available cores. As simple as it may seem, we found that this invari- ant is often broken in Lin ...

Assoc Computing Machinery2016

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

In this paper, a new formulation in the time domain of the Cooray-Rubinstein expression is presented. This new formulation overcomes the problems related to the previous formulation: The Dirac pulse contained in the kernel of the previous formulation is ex ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

A rigorous formula representing the counterpart of the Cooray-Rubinstein expression in the time domain has recently been derived by the authors. In this paper, the formula is particularized for the cases of a unit step and a linearly rising channel-base cu ...

Institute of Electrical and Electronics Engineers2015

End-to-end kernel learning via generative random Fourier features

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Machine-Learning of Atomic-Scale Properties Based on Physical Principles

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

The Linux Scheduler: A Decade of Wasted Cores

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

The Linux Scheduler: A Decade of Wasted Cores

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform