Publications related to Scanning mobility CCN analysis-A method for fast measurements of size-resolved CCN distributions and activation kinetics

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine- tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new ...

2024

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

2024

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Henri Weisen

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

TAYLOR & FRANCIS INC2023

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

Alireza Moallemi, Robin Lewis Modini, Philippe Giaccari

Polar nephelometers are in situ instruments used to measure the angular distribution of light scattered by aerosol particles. These types of measurements contain substantial information about the properties of the aerosol being probed (e.g. concentrations, ...

2022

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Naman Goel, Amit Sharma

Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

Publish your thesis while respecting copyright rules

Marjorie Platon, Manon Velasco

It is very common to reuse published (or unpublished) content in a thesis (e.g. in the case of the theses made of a compilation of published articles). It is therefore important to ensure that the use (or reuse) is possible and to ask for authorizations fr ...

2021

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Inference on the Angular Distribution of Extremes

Claudio Andri Semadeni

The spectral distribution plays a key role in the statistical modelling of multivariate extremes, as it defines the dependence structure of multivariate extreme-value distributions and characterizes the limiting distribution of the relative sizes of the co ...

EPFL2020

Study of B -> ppi pi

Olivier Schneider, Mingkui Wang, Chao Wang, Tagir Aushev, Sun Hee Kim, Donghyun Kim, Xiao Wang, Lei Li

Using a data sample of 772 x 10(6) B (B) over bar pairs collected on the Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric energy e(+)e(-) collider, we report the observation of B-0 -> p (p) over bar pi(+)pi(-) and the first observation ...

AMER PHYSICAL SOC2020

Scanning mobility CCN analysis-A method for fast measurements of size-resolved CCN distributions and activation kinetics

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Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

Unbiased Monte Carlo cluster updates with autoregressive neural networks

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Publish your thesis while respecting copyright rules

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Inference on the Angular Distribution of Extremes

Study of B -> ppi pi

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Inference on the Angular Distribution of Extremes

Study of B -> ppi pi

Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Publish your thesis while respecting copyright rules