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Publications
Typology of ICU-Healthcare Providers Who Delayed or Declined COVID-19 Vaccination
OBJECTIVES: To assess COVID-19 vaccination rates in ICU-healthcare providers (HCPs) in France and to identify the typology of those who dela… (voir plus)yed or declined vaccination. DESIGN: Cross-sectional study. SETTING: Twenty-one ICUs in France. SUBJECTS: Members of the nursing and medical staff and other allied professionals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six hundred ninety-six of the 950 respondents (73.3%) had undergone a full vaccination schedule. Other HCPs either declined vaccination (n = 112) or delayed vaccination until it became mandatory (n = 142). Factors independently associated with full vaccination were age older than 50 years (odds ratio, 0.25 [95% CI, 0.12–0.51]), more than 5 years of ICU experience (0.66 [0.47–0.93]), increasing working time during the surge (0.94 [0.88–1.00]), and spending time with the family (0.92 [0.85–0.99]). Conversely, being a nurse (1.94 [1.25–2.99]) or a nurse assistant (2.77 [1.62–4.73]), and feeling not supported by hospital and ICU directors (1.49 [1.01–2.20]) was independently associated with not being vaccinated. CONCLUSIONS: These results are important to take into account to better implement vaccination strategies in HCPs for existing or future pandemics.
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees… (voir plus) (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neura… (voir plus)l network architectures. Recent works have experimentally shown that two different solutions found after two runs of a stochastic training are often connected by very simple continuous paths (e.g., linear) modulo a permutation of the weights. In this paper, we provide a framework theoretically explaining this empirical observation. Based on convergence rates in Wasserstein distance of empirical measures, we show that, with high probability, two wide enough two-layer neural networks trained with stochastic gradient descent are linearly connected. Additionally, we express upper and lower bounds on the width of each layer of two deep neural networks with independent neuron weights to be linearly connected. Finally, we empirically demonstrate the validity of our approach by showing how the dimension of the support of the weight distribution of neurons, which dictates Wasserstein convergence rates is correlated with linear mode connectivity.
We study the applicability of mixture of parameter-efficient experts (MoPEs) for instruction-tuning large decoder-only language models. Rece… (voir plus)nt literature indicates that MoPEs might enhance performance in specific multi-task instruction-following datasets. In this paper, we extend such previous results and study applicability of MoPEs in settings previously overlooked: a) with open-domain instruction-following datasets; b) with recent decoder-only models and c) with downstream out-of-distribution test sets. We build on top of LLaMA1-13B/-7B and LLaMA2-13B. We study different variants of learned routing, namely per-example routing ([PE]), and a more expensive per-token ([PT]) routing. Overall, we are unable to substantiate strong performance gains observed in related studies in our setting. We observe occasional enhancements of LLAMA2 fine-tuned on Open Platypus dataset in 0-shot SNI evaluation and TruthfulQA evaluation after fine-tuning on a subset of Flan. We shed some light on the inner workings of MoPEs by comparing different routing strategies. We find that [PE] routing tends to collapse at downstream evaluation time reducing the importance of router's application.
We plan to publicly release our code.
It is widely known that it is possible to implant backdoors into neural networks,
by which an attacker can choose an input to produce a part… (voir plus)icular undesirable output
(e.g.\ misclassify an image).
We propose to use \emph{meta-models}, neural networks that take another network's parameters
as input, to detect backdoors directly from model weights.
To this end we present a meta-model architecture and train it on a dataset of approx.\ 4000 clean and backdoored CNNs trained on CIFAR-10.
Our approach is simple and scalable, and is able to detect the presence of a backdoor with
It is widely known that it is possible to implant backdoors into neural networks,
by which an attacker can choose an input to produce a part… (voir plus)icular undesirable output
(e.g.\ misclassify an image).
We propose to use \emph{meta-models}, neural networks that take another network's parameters
as input, to detect backdoors directly from model weights.
To this end we present a meta-model architecture and train it on a dataset of approx.\ 4000 clean and backdoored CNNs trained on CIFAR-10.
Our approach is simple and scalable, and is able to detect the presence of a backdoor with
Zero-shot coordination (ZSC) is a popular setting for studying the ability of AI agents to coordinate with novel partners. Prior formulation… (voir plus)s of ZSC make the assumption that the problem setting is common knowledge i.e. each agent has the knowledge of the underlying Dec-POMDP, every agent knows the others have this knowledge, and so on ad infinitum. However, in most real-world situations, different agents are likely to have different models of the (real world) environment, thus breaking this assumption. To address this limitation, we formulate the _noisy zero-shot coordination_ (NZSC) problem, where agents observe different noisy versions of the ground truth Dec-POMDP generated by passing the true Dec-POMDP through a noise model. Only the distribution of the ground truth Dec-POMDPs and the noise model are common knowledge. We show that any noisy ZSC problem can be reformulated as a ZSC problem by designing a meta-Dec-POMDP with an augmented state space consisting of both the ground truth Dec-POMDP and its corresponding state. In our experiments, we analyze various aspects of NZSC and show that achieving good performance in NZSC requires agents to make use of both the noisy observations of ground truth Dec-POMDP, knowledge of each other's noise models and their interactions with the ground truth Dec-POMDP. Through experimental results, we further establish that ignoring the noise in problem specification can result in sub-par ZSC coordination performance, especially in iterated scenarios. On the whole, our work highlights that NZSC adds an orthogonal challenge to traditional ZSC in tackling the uncertainty about the true problem.
