18 research outputs found
Make the Most Out of Your Net: Alternating Between Canonical and Hard Datasets for Improved Image Demosaicing
Image demosaicing is an important step in the image processing pipeline for
digital cameras, and it is one of the many tasks within the field of image
restoration. A well-known characteristic of natural images is that most patches
are smooth, while high-content patches like textures or repetitive patterns are
much rarer, which results in a long-tailed distribution. This distribution can
create an inductive bias when training machine learning algorithms for image
restoration tasks and for image demosaicing in particular. There have been many
different approaches to address this challenge, such as utilizing specific
losses or designing special network architectures. What makes our work is
unique in that it tackles the problem from a training protocol perspective. Our
proposed training regime consists of two key steps. The first step is a
data-mining stage where sub-categories are created and then refined through an
elimination process to only retain the most helpful sub-categories. The second
step is a cyclic training process where the neural network is trained on both
the mined sub-categories and the original dataset. We have conducted various
experiments to demonstrate the effectiveness of our training method for the
image demosaicing task. Our results show that this method outperforms standard
training across a range of architecture sizes and types, including CNNs and
Transformers. Moreover, we are able to achieve state-of-the-art results with a
significantly smaller neural network, compared to previous state-of-the-art
methods
Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large language models (LLM) to
expand an existing ontology in a semi-automated fashion. We demonstrate our
approach on the biomedical ontology SNOMED-CT utilizing semantic relation types
from the widely used UMLS semantic network. We propose a method that uses
conversational interactions with an LLM to analyze clinical practice guidelines
(CPGs) and detect the relationships among the new medical concepts that are not
present in SNOMED-CT. Our initial experimentation with the conversational
prompts yielded promising preliminary results given a manually generated gold
standard, directing our future potential improvements.Comment: Presented as a short paper at the Knowledge Representation for
Healthcare 2023 worksho
Fundamental performance limits for ideal decoders in high-dimensional linear inverse problems
This paper focuses on characterizing the fundamental performance limits that
can be expected from an ideal decoder given a general model, ie, a general
subset of "simple" vectors of interest. First, we extend the so-called notion
of instance optimality of a decoder to settings where one only wishes to
reconstruct some part of the original high dimensional vector from a
low-dimensional observation. This covers practical settings such as medical
imaging of a region of interest, or audio source separation when one is only
interested in estimating the contribution of a specific instrument to a musical
recording. We define instance optimality relatively to a model much beyond the
traditional framework of sparse recovery, and characterize the existence of an
instance optimal decoder in terms of joint properties of the model and the
considered linear operator. Noiseless and noise-robust settings are both
considered. We show somewhat surprisingly that the existence of noise-aware
instance optimal decoders for all noise levels implies the existence of a
noise-blind decoder. A consequence of our results is that for models that are
rich enough to contain an orthonormal basis, the existence of an L2/L2 instance
optimal decoder is only possible when the linear operator is not substantially
dimension-reducing. This covers well-known cases (sparse vectors, low-rank
matrices) as well as a number of seemingly new situations (structured sparsity
and sparse inverse covariance matrices for instance). We exhibit an
operator-dependent norm which, under a model-specific generalization of the
Restricted Isometry Property (RIP), always yields a feasible instance
optimality property. This norm can be upper bounded by an atomic norm relative
to the considered model.Comment: To appear in IEEE Transactions on Information Theor
Compressed Sensing and Best Approximation from Unions of Subspaces: Beyond Dictionaries
to appear in EUSIPCO 2013International audienceWe propose a theoretical study of the conditions guaranteeing that a decoder will obtain an optimal signal recovery from an underdetermined set of linear measurements. This special type of performance guarantee is termed instance optimality and is typically related with certain properties of the dimensionality-reducing matrix M. Our work extends traditional results in sparse recovery, where instance optimality is expressed with respect to the set of sparse vectors, by replac- ing this set with an arbitrary finite union of subspaces. We show that the suggested instance optimality is equivalent to a generalized null space property of M and discuss possible relations with generalized restricted isometry properties
Compressed Sensing and Best Approximation from Unions of Subspaces: Beyond Dictionaries
to appear in EUSIPCO 2013International audienceWe propose a theoretical study of the conditions guaranteeing that a decoder will obtain an optimal signal recovery from an underdetermined set of linear measurements. This special type of performance guarantee is termed instance optimality and is typically related with certain properties of the dimensionality-reducing matrix M. Our work extends traditional results in sparse recovery, where instance optimality is expressed with respect to the set of sparse vectors, by replac- ing this set with an arbitrary finite union of subspaces. We show that the suggested instance optimality is equivalent to a generalized null space property of M and discuss possible relations with generalized restricted isometry properties
Generalized Null Space and Restricted Isometry Properties
International audienceWe propose a theoretical study of the conditions guar- anteeing that a decoder will obtain an optimal signal recovery from an underdetermined set of linear measurements. This special type of performance guarantee is termed instance optimality and is typically related with certain properties of the dimensionality-reducing matrix M. Our work extends traditional results in sparse recovery, where instance optimality is expressed with respect to the set of sparse vectors, by replacing this set with an arbitrary finite union of subspaces. We show that the suggested instance optimality is equivalent to a generalized null space property of M and discuss possible relations with generalized restricted isometry properties