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pro vyhledávání: '"Dolatabadi A"'
Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias, systematic er
Externí odkaz:
http://arxiv.org/abs/2408.13295
In this study, we introduce the application of causal disparity analysis to unveil intricate relationships and causal pathways between sensitive attributes and the targeted outcomes within real-world observational data. Our methodology involves emplo
Externí odkaz:
http://arxiv.org/abs/2407.02702
Autor:
Roy, Shuvendu, Parhizkar, Yasaman, Ogidi, Franklin, Khazaie, Vahid Reza, Colacci, Michael, Etemad, Ali, Dolatabadi, Elham, Afkanpour, Arash
We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following research questions: (i) How transferable are general-domain representa
Externí odkaz:
http://arxiv.org/abs/2406.07450
FAIIR: Building Toward A Conversational AI Agent Assistant for Youth Mental Health Service Provision
Autor:
Obadinma, Stephen, Lachana, Alia, Norman, Maia, Rankin, Jocelyn, Yu, Joanna, Zhu, Xiaodan, Mastropaolo, Darren, Pandya, Deval, Sultan, Roxana, Dolatabadi, Elham
The world's healthcare systems and mental health agencies face both a growing demand for youth mental health services, alongside a simultaneous challenge of limited resources. Here, we focus on frontline crisis support, where Crisis Responders (CRs)
Externí odkaz:
http://arxiv.org/abs/2405.18553
For the first time, we explore few-shot tuning of vision foundation models for class-incremental learning. Unlike existing few-shot class incremental learning (FSCIL) methods, which train an encoder on a base session to ensure forward compatibility f
Externí odkaz:
http://arxiv.org/abs/2405.16625
The problem of constructing optimal AIFV codes is a special case of that of constructing minimum cost Markov Chains. This paper provides the first complete proof of correctness for the previously known iterative algorithm for constructing such Markov
Externí odkaz:
http://arxiv.org/abs/2405.06831
It is possible to improve upon Tunstall coding using a collection of multiple parse trees. The best such results so far are Iwata and Yamamoto's maximum cost AIVF codes. The most efficient algorithm for designing such codes is an iterative one that c
Externí odkaz:
http://arxiv.org/abs/2405.06805
Autor:
Akbari, Saieed, Dolatabadi, Reza Hosseini, Jamaali, Mohsen, Klavžar, Sandi, Movarraei, Nazanin
The $\Delta$-edge stability number ${\rm es}_{\Delta}(G)$ of a graph $G$ is the minimum number of edges of $G$ whose removal results in a subgraph $H$ with $\Delta(H) = \Delta(G)-1$. Sets whose removal results in a subgraph with smaller maximum degre
Externí odkaz:
http://arxiv.org/abs/2403.05254
Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing defensive measures can only mitigate less than half of these attacks at
Externí odkaz:
http://arxiv.org/abs/2402.13517
Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outputs. Shortcut learning is ubiquitous among many failure cases of neural
Externí odkaz:
http://arxiv.org/abs/2402.11237