Results 81 to 90 of about 36,022 (270)
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations
, 2022 Owing much to the revolution of information technology, the recent progress of deep learning benefits incredibly from the vastly enhanced access to data available in various digital formats. However, in certain scenarios, people may not want their data being used for training commercial models and thus studied how to attack the learnability of deep ...
Peng, Weiqi, Chen, Jinghui
openaire +2 more sources
Molecular Oncology, EarlyView.To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley +1 more source
Is Negation Negative? (And a Discussion of Negative Concord in SOV Languages)
LanguagesIs negation negative? For some authors, in some languages, it is not. This is the case for so-called strict negative concord languages (e.g., Russian), in which negation is taken to be non-negative, following the cross-linguistic analysis for negative ...
Paloma Jeretič
doaj +1 more source
On that one poverty of the stimulus argument
Nordlyd: Tromsø University Working Papers on Language & Linguistics, 2008 This paper examines the logical problem of language acquisition drawing upon an experimental study on children’s knowledge of anaphoric one by Lidz, Waxman and Freedman (2003). The finding was that, upon being presented with the instruction “Look!
Andrea Gualmini
doaj +1 more source
Maximal Machine Learnable Classes
Journal of Computer and System Sciences, 1999 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Case, John, Fulk, Mark A.
openaire +2 more sources
Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers
Molecular Oncology, EarlyView.This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel +6 more
wiley +1 more source
Passive Learning with Target Risk [PDF]
, 2013 In this paper we consider learning in passive setting but with a slight modification. We assume that the target expected loss, also referred to as target risk, is provided in advance for learner as prior knowledge.
Jin, Rong, Mahdavi, Mehrdad
core
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Determinacy, Learnability, and Monetary Policy Inertia [PDF]
We evaluate Taylor-type monetary policy rules from the perspective of which classes of rules most reliably induce determinacy and learnability of a rational expectations equilibrium.
James Bullard, Kaushik Mitra
core
Analyzing library collections with starfield visualizations [PDF]
, 2004 This paper presents a qualitative and formative study of the uses of a starfield-based visualization interface for analysis of library collections.
Nichols, David M. +3 more
core +1 more source